Method and apparatus for detecting fine residual disease by using tumor information

Abstract

The present disclosure relates to a method for detecting a fine residual disease by using tumor information. The method for detecting a fine residual disease by using tumor information comprises the steps of: obtaining first sequencing data associated with a first sample of a patient; obtaining second sequencing data associated with a second sample of the patient; obtaining third sequencing data associated with a third sample of the patient; and detecting a micro residual disease of the patient on the basis of the first sequencing data, the second sequencing data and the third sequencing data.

Description

Method and device for detecting minimal residual disease using tumor information

[0001] The present disclosure relates to a method and device for detecting minimal residual disease using tumor information, and more particularly, to a method and device for detecting minimal residual disease in a patient using mutation information of tumor tissue and a liquid biopsy sample.

[0002] Genetic analysis technology is widely used in the medical field, such as identifying a living organism's genes to determine its characteristics or traits. Recently, medical practices for treating various diseases, including cancer, are evolving from traditional prescription-based approaches to precision medicine, a form of personalized treatment that considers each patient's genetic information and health history.

[0003] In the field of precision medicine, acquiring massive amounts of personal genetic information and performing related clinical analyses is crucial. Recently, Next-Generation Sequencing (NGS), a technique capable of rapidly reading large amounts of DNA information in parallel, has been widely used in various medical fields, including cancer screening. While NGS offers the advantages of being inexpensive, time-consuming, and convenient for genome analysis, it inherently suffers from a background error rate of 0.1% to 1%. This means that even in DNA without mutations, the sequencing process can statistically incorrectly identify mutations in one out of every 100 to 1,000 base pairs (i.e., an error rate of 0.1% to 1%) (False Positive Mutation).

[0004] Due to these inherent limitations of NGS techniques, they have limited ability to detect DNA mutations present at very low concentrations. For example, cancer-derived DNA (e.g., circulating tumor DNA: ctDNA) is typically present in the blood of cancer patients at concentrations of less than 1%. Therefore, it is difficult to easily distinguish whether mutations detected in blood using NGS are derived from cancer tissue or are false positives due to background error rates. Therefore, there is a need for new techniques that can detect DNA mutations present at very low concentrations in patient blood (such as tumor cell remnants) by reducing the background error rate.

[0005] The present disclosure provides a method for detecting residual microscopic disease using tumor information to solve the above-described problems, a computer-readable non-transitory recording medium recording commands, and a device (system).

[0006] The present disclosure can be implemented in various ways, including a computer-readable, non-transitory recording medium having recorded thereon a method, system (device), or instructions.

[0007] A method for detecting minimal residual disease, performed by at least one processor according to one embodiment of the present disclosure, comprises the steps of: obtaining first sequencing data associated with a first sample of a patient; obtaining second sequencing data associated with a second sample of the patient; obtaining third sequencing data associated with a third sample of the patient; and performing minimal residual disease detection for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data.

[0008] In one embodiment of the present disclosure, the first sequencing data, the second sequencing data, and the third sequencing data are obtained through whole genome sequencing (WGS).

[0009] In one embodiment of the present disclosure, the first sample is a tumor tissue biopsy sample of the patient, the second sample is a normal blood sample of the patient, and the third sample is a plasma sample of the patient, wherein the plasma sample includes cell free Deoxyribo Nucleic Acid (cfDNA) and circulating tumor Deoxyribo Nucleic Acid (ctDNA).

[0010] In one embodiment of the present disclosure, the first sample and the second sample are samples obtained at a first time point, the third sample is a sample obtained at a second time point after the first time point, and at least one of surgery or treatment is performed on the patient after the first time point and before the second time point.

[0011] In one embodiment of the present disclosure, the method further includes a step of detecting tumor tissue mutation information of a patient by comparing first sequencing data and second sequencing data, and a step of performing background error filtering on the detected tumor tissue mutation information using genetic data associated with a plurality of sample patients that are distinct from the patient.

[0012] In one embodiment of the present disclosure, the genetic data includes sequencing data for normal blood samples of a plurality of sample patients, and the step of performing filtering includes a step of removing, from the tumor tissue mutation information, mutations detected in the sequencing data for normal blood samples of the plurality of sample patients among the detected tumor tissue mutations.

[0013] In one embodiment of the present disclosure, the genetic data includes sequencing data for plasma samples of a plurality of sample patients, and the step of performing filtering includes a step of removing, from the tumor tissue mutation information, mutations detected in the sequencing data for plasma samples of the plurality of sample patients among the detected tumor tissue mutations.

[0014] In one embodiment of the present disclosure, the step of performing minimal residual disease detection includes the step of calculating a limit of detection (Limit of Detection) value for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data, the step of calculating a tumor cell fraction (TCF) for the patient based on the third sequencing data, the step of correcting the tumor cell fraction using genetic data associated with a plurality of sample patients that are distinct from the patient, and the step of determining whether the patient has a tumor recurrence based on the corrected tumor cell fraction and the limit of detection.

[0015] In one embodiment of the present disclosure, the Limit of Detection value represents the lowest tumor cell percentage that can be detected in a patient's plasma sample.

[0016] In one embodiment of the present disclosure, the detection limit is calculated based on the number of tumor tissue mutations of the patient detected by comparing the first sequencing data and the second sequencing data and the average sequencing depth of the third sequencing data.

[0017] In one embodiment of the present disclosure, the step of calculating the tumor cell ratio includes the step of determining the number of reads that are different from a reference sequence in the third sequencing data, the step of determining the number of reads that match the reference sequence in the third sequencing data, and the step of calculating the tumor cell ratio based on the number of reads that are different from the reference sequence and the number of reads that match the reference sequence.

[0018] In one embodiment of the present disclosure, the step of determining a different number of reads includes a step of detecting tumor tissue mutation information of a patient by comparing first sequencing data and second sequencing data, a step of determining a number of reads including a mutation detected in tumor tissue in third sequencing data based on the tumor tissue mutation information, and a step of using the number of reads including a mutation detected in tumor tissue in the third sequencing data as the number of reads different from a reference sequence.

[0019] In one embodiment of the present disclosure, the step of correcting the tumor cell ratio includes the step of determining the number of reads that are different from a reference sequence in plasma sequencing data included in genetic data associated with a plurality of sample patients that are distinct from the patient, the step of determining the number of reads that match the reference sequence in plasma sequencing data included in genetic data associated with a plurality of sample patients that are distinct from the patient, the step of calculating a random error rate based on the number of reads that are different from the reference sequence and the number of reads that match the reference sequence, and the step of correcting the tumor cell ratio using the random error rate.

[0020] In one embodiment of the present disclosure, the step of determining whether a patient's tumor has recurred includes the step of calculating a confidence interval of a predetermined confidence level of a corrected tumor cell ratio, and the step of determining that the patient's tumor has recurred in response to determining that a lower limit of the calculated confidence interval is higher than a calculated detection limit.

[0021] In one embodiment of the present disclosure, the step of performing minimal residual disease detection further includes the step of generating a chromosomal arm-level copy number profile of a first chromosomal arm based on first sequencing data, the step of generating a copy number profile of a second chromosomal arm based on second sequencing data, the step of generating a copy number profile of a third chromosomal arm based on third sequencing data, and the step of calculating a tumor cell ratio based on the copy number profiles of the first to third chromosomal arms.

[0022] In one embodiment of the present disclosure, the step of performing minimal residual disease detection further includes the step of identifying a first set of structural variants detected from the first sequencing data, the step of identifying a second set of structural variants detected from the third sequencing data, and the step of determining whether the patient has a tumor recurrence based on a comparison result between the first set of structural variants and the second set of structural variants.

[0023] In one embodiment of the present disclosure, the step of determining whether a patient's tumor has recurred includes the step of determining that the patient's tumor has recurred if the number or ratio of structural mutations included in the second set of structural mutations among the first set of structural mutations is greater than or equal to a threshold.

[0024] In one embodiment of the present disclosure, the first set of structural variations comprises at least one of an inversion, a translocation, a duplication, a deletion, or an insertion of a region of the genome.

[0025] A computer-readable non-transitory recording medium having recorded thereon commands for executing a method for detecting residual microscopic disease using tumor information according to one embodiment of the present disclosure is provided.

[0026] A system according to one embodiment of the present disclosure comprises a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program contained in the memory, wherein the at least one program comprises instructions for obtaining first sequencing data associated with a first sample of a patient, obtaining second sequencing data associated with a second sample of the patient, obtaining third sequencing data associated with a third sample of the patient, and performing minimal residual disease detection for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data.

[0027] According to various embodiments of the present disclosure, tumor cells can be detected earlier by lowering the threshold for detecting minimal residual disease (MRD) in noninvasive liquid biopsy samples by reducing the background error rate through data filtering.

[0028] According to various embodiments of the present disclosure, the time and computing resources required to compare the entire plasma sequencing data with a reference sequence can be reduced by calculating the tumor cell ratio based on filtered tumor tissue mutations.

[0029] According to various embodiments of the present disclosure, by filtering out tumor tissue mutations of a target patient using genetic data of a sample patient, the proportion of tumor cells in the body of a target patient can be predicted with high accuracy.

[0030] According to various embodiments of the present disclosure, the proportion of tumor cells in the body of a target patient can be predicted with high accuracy by utilizing the copy number profile at the chromosome arm level of the target patient.

[0031] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs (referred to as “one skilled in the art”) from the description of the claims.

[0032] Embodiments of the present disclosure will be described below with reference to the accompanying drawings, wherein like reference numerals represent similar elements, but are not limited thereto.

[0033] FIG. 1 is a diagram illustrating an example of a process for performing microscopic residual disease detection on a target patient according to one embodiment of the present disclosure.

[0034] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to enable communication with a plurality of user terminals in order to provide a microscopic residual disease detection service utilizing tumor information according to one embodiment of the present disclosure.

[0035] FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure.

[0036] FIG. 4 is a diagram illustrating an example of a graph of the tumor cell ratio in a subject patient over time according to one embodiment of the present disclosure.

[0037] FIG. 5 is a diagram showing a blood sample of a target patient according to one embodiment of the present disclosure.

[0038] FIG. 6 is a diagram showing a process for calculating a detection limit for a target patient according to one embodiment of the present disclosure.

[0039] FIG. 7 is a diagram illustrating a tumor detection process according to one embodiment of the present disclosure.

[0040] FIG. 8 is a diagram illustrating an example of a graph showing the relationship between the number of mutations detected in tumor cells of a target patient and the detection limit of the target patient according to one embodiment of the present disclosure.

[0041] FIG. 9 is a diagram illustrating an example of generating an arm unit copy number profile according to one embodiment of the present disclosure.

[0042] FIG. 10 is a diagram illustrating an example of calculating the tumor cell ratio in a target patient's body using a copy number profile at the chromosome arm level according to one embodiment of the present disclosure.

[0043] FIG. 11 is a diagram showing the performance verification results of a method for detecting residual microscopic disease in a target patient using genetic data of a sample patient according to one embodiment of the present disclosure.

[0044] FIG. 12 is a diagram showing the results of performance verification of a method for detecting microscopic residual disease in a target patient using a copy number profile at the chromosome arm level of the target patient according to one embodiment of the present disclosure.

[0045] FIG. 13 is a flowchart illustrating a method for detecting residual microscopic disease using tumor information according to one embodiment of the present disclosure.

[0046] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions of widely known functions or configurations will be omitted if they may unnecessarily obscure the gist of the present disclosure.

[0047] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Furthermore, in the description of the embodiments below, duplicate descriptions of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0048] The advantages and features of the disclosed embodiments, and methods for achieving them, will become clearer with reference to the embodiments described below, along with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided solely to ensure the completeness of the disclosure and to fully inform those skilled in the art of the scope of the invention.

[0049] The terms used in this specification will be briefly explained, followed by a detailed description of the disclosed embodiments. The terms used in this specification have been selected from widely used, current terms, taking into account the functions of the present disclosure. However, these terms may vary depending on the intentions of engineers working in the relevant field, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should not be defined simply as names of terms, but rather based on their meanings and the overall content of the present disclosure.

[0050] In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, plural expressions include singular expressions unless the context clearly indicates otherwise. When a part of the specification is said to include a component, this does not exclude other components, but rather implies that other components may be included, unless otherwise specifically stated.

[0051] Also, the term 'module' or 'part' used in the specification means a software or hardware component, and the 'module' or 'part' performs certain roles. However, the 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside on an addressable storage medium and may be configured to execute one or more processors. Thus, as an example, the 'module' or 'part' may include at least one of components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. The functionality provided within the components and 'modules' or 'parts' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0052] According to one embodiment of the present disclosure, a 'module' or 'unit' may be implemented as a processor and a memory. 'Processor' should be broadly construed to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, and the like. In some circumstances, a 'processor' may also refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable gate array (FPGA), and the like. A 'processor' may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or any other such combination of configurations. In addition, 'memory' should be broadly construed to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. Memory is said to be in electronic communication with the processor if the processor can read information from, and / or write information to, the memory. Memory integrated in a processor is in electronic communication with the processor.

[0053] In this disclosure, "Whole Genome Sequencing (WGS)" or "whole genome sequencing" may refer to a technique used to determine the entire DNA sequence of an organism's genome. Specifically, whole genome sequencing may involve deciphering and identifying the order of nucleotide bases (adenine, cytosine, guanine, and thymine) within the entire set of genetic material of a human or organism. The entire set of genetic material may include all genes, noncoding regions, and any additional genetic elements present within the genome. In one embodiment, whole genome sequencing may be performed in several steps. For example, whole genome sequencing may be performed by extracting DNA from a specific cell, fragmenting the extracted DNA into smaller pieces, and generating millions or billions of short DNA sequences, referred to as "reads." The resulting reads may be aligned and assembled to reconstruct the entire genome sequence. In certain embodiments, various duplex sequencing methods can be used during whole-genome sequencing to minimize background error rates, enabling the detection of even minute amounts of cancer-derived DNA in blood. CODEC (Concatenating Original Duplex for Error Correction), an example of duplex sequencing, can be used during whole-genome sequencing. PCT Publication No. WO 2022 / 125997 A1, published on June 16, 2022, is incorporated herein by reference.

[0054] In the present disclosure, 'DNA' is not limited to DNA itself, and may include any nucleic acid or nucleic acid sequence such as RNA (RiboNucleic Acid).

[0055] In this disclosure, "cfDNA" may refer to circulating free DNA, also known as cell-free DNA or circulating DNA. cfDNA may refer to small fragments of DNA released into the bloodstream and other bodily fluids by cells undergoing normal cell death or as a result of certain pathological processes. cfDNA may be composed of short DNA fragments derived from various tissues of the body, including healthy and diseased cells. cfDNA may originate from organs such as the liver, heart, or lungs, or from tumors (e.g., malignant tumors such as cancer). For example, a blood sample may be centrifuged to obtain a plasma layer, a buffy coat layer, and a red blood cell layer from the top layer, and cfDNA may be obtained from the plasma layer.

[0056] In this disclosure, "ctDNA" may refer to circulating tumor DNA. ctDNA may be a subset of cfDNA derived from tumor cells. Tumor cells may shed ctDNA into the bloodstream upon undergoing apoptosis or cell death. ctDNA may carry genetic alterations or mutations characteristic of the tumor from which it originated. ctDNA can provide useful information about the genetic characteristics and mutation profile of a tumor without the need for invasive tissue biopsies. Analyzing ctDNA can monitor a patient's treatment response and detect minimal residual disease (MRD).

[0057] In this disclosure, the term "normal blood sample" or "buffy coat" may refer to a specific component of a blood sample, including white blood cells and platelets. For example, a normal blood sample may be obtained by centrifuging a blood sample. The normal blood sample may be obtained from the thin layer formed between the red blood cell layer at the bottom of the centrifuged sample and the plasma layer at the top. A normal blood sample may contain a higher concentration of nucleated cells, including white blood cells with nuclei containing DNA, providing a source of genomic DNA derived from cells of a specific individual. Therefore, when performing WGS, DNA can be extracted from a normal blood sample to obtain genomic DNA of a specific individual, which can be used as reference or control DNA to compare and identify genetic variations or mutations present in cfDNA or ctDNA extracted from the same individual, or in DNA of tumor tissue extracted from the same individual. It can be assumed that a normal blood sample does not contain cancer cells.

[0058] In the present disclosure, 'sequencing depth' or sequencing coverage may refer to the average number of times each base of a genome is read during a sequencing process. For example, sequencing depth may indicate how many times a specific nucleotide (A, T, C, or G) at a specific location in the genome has been sequenced. Sequencing depth is a parameter that affects the accuracy and reliability of sequencing data. Sequencing depth is typically measured as "X," where 1X coverage may mean that each base has been sequenced once on average, and 10X coverage may mean that each base has been sequenced 10 times on average.

[0059] In the present disclosure, 'copy number profiling' may refer to a technology for obtaining a copy number profile by analyzing the copy number (duplication number) of a specific DNA segment or region within a genome in WGS data.

[0060] In one embodiment, copy number profiling may involve several steps. First, a DNA isolation step may be performed to isolate DNA from cells or tissues of interest. Next, a sequencing step may be performed to divide the extracted DNA into smaller fragments (fragmentation) and sequence the DNA fragments. This step can generate a large number of short DNA sequences, called reads. In some embodiments, after fragmenting the DNA and before sequencing, a step may be performed to amplify and replicate the DNA fragments using a technique such as PCR (Polymerase Chain Reaction).

[0061] The generated reads can then undergo a read alignment step, where they are aligned or mapped to a reference sequence. This step can involve comparing the reads to known DNA sequences in the reference sequence to determine the read's origin. Once the reads are aligned, a copy number analysis step can be performed using software tools to analyze the genome-wide sequencing coverage or depth. Sequencing coverage can represent the number of reads aligned to a specific genomic region. A normalization step can then be performed on the coverage data to account for variations in sequencing depth and other technical biases. This allows for accurate comparisons between different genomic regions.

[0062] Afterwards, a statistical algorithm can be applied to the normalized coverage data to perform a copy number calling step, which identifies genomic regions exhibiting copy number variation. This algorithm can compare the observed coverage to the expected coverage based on the diploid genome. The copy number profile can then be visualized using a plot or heatmap showing genomic regions with copy number gains or losses, a step referred to as the visualization and interpretation step. This visualization data can provide insights into genomic alterations such as amplifications, deletions, or duplications.

[0063] In this disclosure, the "Limit of Detection" may refer to the minimum detectable concentration that can confirm the presence or absence of a target substance in an analysis process. For example, the "Limit of Detection" may refer to the lowest tumor cell ratio detectable in a plasma sample from a tumor patient.

[0064] FIG. 1 is a diagram illustrating an example of a process for performing detection of residual microscopic disease on a target patient (110) according to one embodiment of the present disclosure. The target patient (110) may be a patient with tumor tissue (e.g., cancer tissue). Furthermore, the target patient (110) may be a subject for whom detection of residual microscopic disease is performed after undergoing treatment (120, 130) or surgery (140) on the tumor tissue.

[0065] In one embodiment, at a first point in time prior to performing treatment (120, 130) or surgery (140) on tumor tissue, a tumor tissue biopsy sample and a normal blood sample may be collected from the subject patient (110). The normal blood sample may be obtained by centrifuging blood collected from the subject patient (110). Thereafter, tumor tissue sequencing data (112) and normal blood sequencing data (114) may be generated based on the tumor tissue biopsy sample and the normal blood sample. Here, each sequencing data may be obtained through whole genome sequencing (WGS).

[0066] Thereafter, tumor tissue mutation information of the target patient (110) can be detected based on the acquired tumor tissue sequencing data (112) and normal blood sequencing data (114). For the detected tumor tissue mutation information, background error filtering can be performed using genetic data associated with a plurality of sample patients that are distinct from the target patient (110). As background error filtering is performed, the background error rate is reduced, so that whether the target patient (110) has a tumor recurrence, etc. can be confirmed at an earlier point in time than before background error filtering. The specific process of detecting and filtering tumor tissue mutation information will be described in detail below using FIG. 6.

[0067] In one embodiment, a tumor tissue biopsy sample and a normal blood sample may be collected from a subject patient (110), and then at least one of a treatment (120, 130) and / or a surgery (140) may be performed on the subject patient (110). As the treatment (120, 130) and / or the surgery (140) is performed on the subject patient (110), the percentage of tumor cells in the subject patient's body (110) may decrease. However, over time, the percentage of tumor cells in the subject patient's body (110) may increase again.

[0068] To quickly respond to disease recurrence due to an increase in tumor cells in the body, it is important to detect an increase in the tumor cell ratio early. To this end, a plasma sample may be collected from the subject patient (110) after treatment (120, 130) and / or surgery (140). The plasma sample may be collected by centrifuging blood (122, 132, 142, 146) collected from the subject patient (110). The plasma sample may include cell-free deoxyribonucleic acid (cfDNA) and circulating tumor deoxyribonucleic acid (ctDNA). The plasma sample may be collected and analyzed from the subject patient (110) periodically or as needed, thereby being used for follow-up testing to determine whether the tumor has recurred. Specifically, plasma sample sequencing data (124, 134, 144, 148) is generated based on the plasma sample contained in the blood (122, 132, 142, 146), and based on the obtained plasma sample sequencing data (124, 134, 144, 148), whether additional treatment and / or surgery is required for the target patient (110) can be determined.

[0069] For example, plasma sample sequencing data (124) can be obtained from blood (122) collected at a second time point after the first treatment (120). Thereafter, based on the tumor tissue mutation information and the plasma sample sequencing data (124), a detection limit value of the tumor tissue of the target patient (110) is calculated, and detection of minimal residual disease of the target patient (110) can be performed. The specific process of calculating the detection limit value of the tumor tissue of the target patient (110) will be described in detail below using FIG. 6. In addition, the specific process of performing detection of minimal residual disease for the target patient (110) will be described in detail below using FIG. 7.

[0070] If minimal residual disease is detected in the subject patient (110) after the first treatment (120) (or if the tumor is determined to have recurred), a second treatment (130) may be performed / executed. Thereafter, at a third time point after the second treatment (130) is performed, blood (132) may be collected from the subject patient (110), and the aforementioned detection of minimal residual disease may be repeated.

[0071] In another example, minimal residual disease may not be detected based on plasma sample sequencing data (144) obtained from blood (142) collected at a fourth time point after surgery (140). In this case, the subject patient (110) is determined to have no recurrence of the tumor, and no additional treatment and / or surgery is required. Subsequently, minimal residual disease detection may be performed again based on plasma sample sequencing data (148) obtained from blood (146) collected at a fifth time point after the fourth time point.

[0072] That is, blood of a subject patient (110) who has completed treatment (120, 130) and / or surgery (140) may be collected at predetermined intervals or as needed, and plasma sample sequencing data (124, 134, 144, 148) may be generated based on a plasma sample extracted from the collected blood. Subsequently, detection of minimal residual disease may be performed based on the plasma sample sequencing data (124, 134, 144, 148). If minimal residual disease is detected, additional treatment and / or surgery may be performed / executed on the subject patient (110).

[0073] FIG. 2 is a schematic diagram showing a configuration in which an information processing system (230) is connected to a plurality of user terminals (210_1, 210_2, 210_3) so as to be able to communicate with each other in order to provide a minimal residual disease detection service using tumor information according to one embodiment of the present disclosure. The information processing system (230) may include system(s) capable of providing a minimal residual disease detection service using tumor information. In one embodiment, the information processing system (230) may include one or more server devices and / or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to the minimal residual disease detection service using tumor information, or one or more distributed computing devices and / or distributed databases based on cloud computing services. For example, the information processing system (230) may include separate systems (e.g., servers) for the minimal residual disease detection service using tumor information.

[0074] Services for detecting residual microscopic disease using tumor information provided by the information processing system (230) may be provided to users through applications installed on each of a plurality of user terminals (210_1, 210_2, 210_3). For example, the plurality of user terminals (210_1, 210_2, 210_3) may be user terminals of medical institution professionals, user terminals of patients, etc.

[0075] A plurality of user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) via a network (220). The network (220) can be configured to enable communication between the plurality of user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) can be configured as a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a wireless network such as a mobile communication network, WLAN (Wireless LAN), Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include not only a communication method utilizing a communication network (e.g., a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network (220) may include, but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

[0076] In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are illustrated as examples of user terminals, but are not limited thereto, and the user terminals (210_1, 210_2, 210_3) may be any computing device capable of wired and / or wireless communication and capable of installing and executing applications, etc. For example, the user terminals may include smartphones, mobile phones, navigation devices, vehicle black boxes, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, wearable devices, IoT (Internet of Things) devices, VR (virtual reality) devices, AR (augmented reality) devices, etc. In addition, although FIG. 2 illustrates three user terminals (210_1, 210_2, 210_3) communicating with the information processing system (230) via the network (220), this is not limited thereto, and a different number of user terminals may be configured to communicate with the information processing system (230) via the network (220).

[0077] FIG. 3 is a block diagram showing the internal configuration of a user terminal (210) and an information processing system (230) according to one embodiment of the present disclosure. The user terminal (210) may refer to any computing device capable of executing applications and capable of wired / wireless communication, and may include, for example, a mobile phone terminal (210_1), a tablet terminal (210_2), a PC terminal (210_3) of FIG. 2. As illustrated, the user terminal (210) may include a memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include a memory (332), a processor (334), a communication module (336), and an input / output interface (338). As illustrated in FIG. 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and / or data via a network (220) using respective communication modules (316, 336). In addition, the input / output device (320) may be configured to input information and / or data to the user terminal (210) or output information and / or data generated from the user terminal (210) via the input / output interface (318).

[0078] The memory (312, 332) may include any non-transitory computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a non-permanent mass storage device such as a read-only memory (ROM), a disk drive, a solid-state drive (SSD), a flash memory, etc. As another example, a non-permanent mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the user terminal (210) or the information processing system (230) as a separate permanent storage device distinct from the memory. In addition, the memory (312, 332) may store an operating system and at least one program code (e.g., code for a minimal residual disease detection service, etc.).

[0079] These software components may be loaded from a computer-readable recording medium separate from the memory (312, 332). This separate computer-readable recording medium may include a recording medium directly connectable to the user terminal (210) and the information processing system (230), and may include, for example, a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. As another example, the software components may be loaded into the memory (312, 332) through a communication module (316, 336) other than a computer-readable recording medium. For example, at least one program may be loaded into the memory (312, 332) based on a computer program (e.g., an application related to a service for detecting minimal residual disease using tumor information) that is installed by files provided by developers or a file distribution system that distributes installation files of applications through a network (220).

[0080] The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (314, 334) by a memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a storage device such as the memory (312, 332).

[0081] The communication module (316, 336) may provide a configuration or function for the user terminal (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the user terminal (210) and / or the information processing system (230) to communicate with another user terminal or another system (e.g., a separate cloud system, etc.). For example, a request or data (e.g., a request for detection of a residual disease, a request for genetic data, etc.) generated by the processor (314) of the user terminal (210) according to a program code stored in a recording device such as a memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) can be received by the user terminal (210) through the communication module (316) of the user terminal (210) via the communication module (336) and the network (220).

[0082] The input / output interface (318) may be a means for interfacing with an input / output device (320). As an example, the input device may include a device such as a camera, a keyboard, a microphone, a mouse, etc., including an audio sensor and / or an image sensor, and the output device may include a device such as a display, a speaker, a haptic feedback device, etc. As another example, the input / output interface (318) may be a means for interfacing with a device that has a configuration or function integrated into one for performing input and output, such as a touch screen. In FIG. 3, the input / output device (320) is illustrated as not being included in the user terminal (210), but is not limited thereto and may be configured as a single device with the user terminal (210). In addition, the input / output interface (338) of the information processing system (230) may be a means for interfacing with a device (not shown) for input or output that is connected to the information processing system (230) or that the information processing system (230) may include. In FIG. 3, the input / output interface (318, 338) is illustrated as an element configured separately from the processor (314, 334), but is not limited thereto, and the input / output interface (318, 338) may be configured to be included in the processor (314, 334).

[0083] The user terminal (210) and the information processing system (230) may include more components than those shown in FIG. 3. However, it is not necessary to explicitly illustrate most of the conventional technical components. In one embodiment, the user terminal (210) may be implemented to include at least some of the input / output devices (320) described above. In addition, the user terminal (210) may further include other components such as a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, a database, etc. For example, if the user terminal (210) is a smartphone, it may include components that a smartphone generally includes, and for example, various components such as an acceleration sensor, a gyro sensor, a microphone module, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration may be implemented to be further included in the user terminal (210).

[0084] According to one embodiment, the processor (314) of the user terminal (210) may be configured to operate an application or web browser application that provides a service for detecting minimal residual disease using tumor information. At this time, program code associated with the application may be loaded into the memory (312) of the user terminal (210). While the application is operating, the processor (314) of the user terminal (210) may receive information and / or data provided from the input / output device (320) through the input / output interface (318) or may receive information and / or data from the information processing system (230) through the communication module (316), and may process the received information and / or data and store it in the memory (312). In addition, such information and / or data may be provided to the information processing system (230) through the communication module (316). For example, the processor (314) may receive genetic data including reference sequence data, sequencing data for normal blood samples of a plurality of sample patients and / or sequencing data for plasma samples of a plurality of sample patients, results of detection of minimal residual disease, etc., from the information processing system (230).

[0085] While the application is running, the processor (314) can receive voice data, text, images, videos, etc. input or selected through input devices such as a camera, microphone, touch screen, keyboard, audio sensor, and / or image sensor connected to the input / output interface (318), and can store the received voice data, text, images, and / or videos, etc. in the memory (312) or provide them to the information processing system (230) through the communication module (316) and the network (220).

[0086] The processor (314) of the user terminal (210) can output information and / or data by transmitting the information and / or data to an input / output device (320) through an input / output interface (318). For example, the processor (314) of the user terminal (210) can output the processed information and / or data through an output device (320), such as a display output capable device (e.g., a touch screen, a display, etc.) or a voice output capable device (e.g., a speaker).

[0087] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from multiple user terminals (210) and / or multiple external systems. Information and / or data processed by the processor (334) may be provided to the user terminal (210) via a communication module (336) and a network (220).

[0088] FIG. 4 is a diagram illustrating an example of a graph (400) of the tumor cell ratio in a patient over time according to one embodiment of the present disclosure. In one embodiment, the graph (400) may represent the tumor cell ratio in a typical tumor patient over time as an example for explaining the present disclosure.

[0089] Tumor cells in a patient's body can be detected using medical images (e.g., CT images) or ctDNA in the patient's blood. Typically, when detecting tumor cells using medical images, tumors appear on the images only if they are larger than a certain size. Therefore, the first limit of detection (LOD1) for detecting tumor cells using medical images may be greater than the second limit of detection (LOD2) for detecting tumor cells using ctDNA in the patient's blood. In other words, using ctDNA allows for earlier detection of tumor cells (at a lower tumor cell percentage) than using medical images.

[0090] Considering that the presence of a tumor is typically first confirmed through medical imaging, early detection of a tumor is considered possible if the tumor is detected before the patient's tumor cell count reaches the first limit of detection (LOD1) (e.g., between LOD1 and LOD2). In this case, the tumor cell count can be reduced through surgery (e.g., tumor removal) and / or treatment (e.g., drug therapy).

[0091] Even after surgery and / or treatment reduces the tumor cell count, the tumor cell count may increase again over time. For example, if the tumor cell count continues to increase after surgery and / or treatment and exceeds the first limit of detection (LOD1), the tumor may be re-identified on medical imaging. In this case, the tumor may be considered to have "clinically relapsed."

[0092] Therefore, it is necessary to monitor the presence or ratio of tumor cells before clinical recurrence. However, the background error rate during the sequencing process makes it difficult to detect tumor cells using ctDNA before the tumor cell ratio in the body reaches the second detection threshold. In other words, if the tumor cell ratio in the body is below the second detection threshold, it is difficult to easily distinguish whether mutations detected in the blood are derived from tumor cells or are falsely detected due to the background error rate.

[0093] To address this issue, reducing the background error rate through data filtering can lower the second detection threshold, allowing for earlier detection of tumor cells. Specifically, by lowering the threshold for detecting minimal residual disease (MRD) in noninvasive liquid biopsy samples, tumor cells can be detected earlier while significantly reducing the burden on patients.

[0094] FIG. 5 is a diagram illustrating a blood sample (500) of a subject patient according to one embodiment of the present disclosure. The blood sample (500) illustrated in FIG. 5 may be a blood sample (liquid biopsy sample) obtained by centrifuging blood collected from the subject patient and separating the layers. The centrifuged blood sample (500) may include, from the upper layer, a plasma layer (510), a normal blood layer (520), and a red blood cell layer (530).

[0095] Blood samples (500) may be collected from the subject patient before and after surgery and / or treatment. In one embodiment, at least a portion of a normal blood layer (520) may be collected from the blood sample (500) before surgery and / or treatment of the subject patient. Normal blood sequencing data may be obtained from the collected normal blood layer (520) sample by whole genome sequencing. Thereafter, the obtained normal blood sequencing data may be compared with tumor tissue sequencing data obtained from a tumor tissue biopsy sample of the subject patient to detect tumor tissue mutation information of the subject patient.

[0096] In one embodiment, after surgery and / or treatment of a subject patient, at least a portion of a plasma layer (510) may be collected from a blood sample (500). The plasma layer (510) may include cell-free deoxyribonucleic acid (cfDNA) and circulating tumor deoxyribonucleic acid (ctDNA). Whole-genome sequencing may be used to obtain plasma sequencing data from the plasma layer (510) sample. Subsequently, the plasma sequencing data may be used to detect cfDNA or ctDNA, thereby determining the percentage of tumor cells in the subject patient's body and whether tumors have recurred.

[0097] FIG. 6 is a diagram illustrating a process for calculating a limit of detection (LOD) (680) for a target patient according to one embodiment of the present disclosure. The limit of detection (680) for a target patient may represent the lowest tumor cell ratio that can be detected in a plasma sample of the target patient. The limit of detection (680) may vary depending on the number of mutations in the tumor tissue of the target patient, and the higher the accuracy of the sequencing data acquired through whole genome sequencing, the more accurately the limit of detection (680) can be determined.

[0098] As illustrated, the detection limit (680) can be calculated based on tumor tissue sequencing data (610), normal blood sequencing data (620), and plasma sequencing data (660). At this time, the tumor tissue sequencing data (610) can be obtained through whole genome sequencing (WGS) on a tumor tissue biopsy sample collected from a tumor of a target patient. In addition, the normal blood sequencing data (620) can be obtained through whole genome sequencing (WGS) on a normal blood sample collected from a normal blood layer (e.g., 520 of FIG. 5) among blood samples of the target patient. Additionally, the plasma sequencing data (660) can be obtained through whole genome sequencing (WGS) on a plasma sample collected from a plasma layer (e.g., 510 of FIG. 5) among blood samples of the target patient.

[0099] In one embodiment, the detection threshold (680) of the target patient may be calculated based on the tumor tissue variant (630) of the target patient (specifically, the filtered tumor tissue variant (650)) and the average sequencing depth (670) of the plasma sequencing data (660). Specifically, the tumor tissue variant (630) of the target patient, which is the basis for calculating the detection threshold (680), may be detected by comparing the tumor tissue sequencing data (610) with the normal blood sequencing data (620). That is, it may be assumed that the normal blood sample does not contain tumor tissue, and the normal blood sequencing data (620) may be used as reference or control sequencing data for identifying genetic variants or mutations in relation to the tumor tissue sequencing data (610). The tumor tissue variant (630) information may include location information where the variant exists and mutated nucleic acid sequence information.

[0100] Additionally or alternatively, variations in ctDNA in plasma can be detected by comparing normal blood sequencing data (620) with plasma sequencing data (660). That is, normal blood sequencing data (620) can be used as reference or control sequencing data in relation to plasma sequencing data (660). In this case, variations detected in the plasma sequencing data (660) that are not detected in the tumor tissue sequencing data (610) can be additionally removed. In one embodiment, variations detected in the plasma sequencing data (660) can be used to replace the tumor tissue variations (630) of FIG. 6 or added to the tumor tissue variations (630) and then filtered (642, 644), thereby calculating a detection limit (680) for the target patient.

[0101] For the detected tumor tissue mutation (630), background error filtering (642, 644) may be performed on the detected tumor tissue mutation (630) information using genetic data associated with a plurality of sample patients that are distinct from the target patient for detecting minimal residual disease, thereby generating filtered tumor tissue mutation (650). For example, if the target patient is a lung cancer patient, 33 lung cancer patients that are distinct from the target patient may be selected from the plurality of sample patients that are distinct from the target patient, and background error filtering may be performed using the genetic data of the 33 lung cancer patients. The genetic data may include normal blood sequencing data and plasma sequencing data of the plurality of sample patients. Additionally, the genetic data may include tumor tissue sequencing data of the plurality of sample patients. The genetic data associated with the plurality of sample patients that are used for filtering (642, 644) may be obtained from a genetic database (640).

[0102] In one embodiment, the primary filtering (642) of the tumor tissue variant (630) may be performed using sequencing data for normal blood samples of the plurality of sample patients included in the genetic data of the plurality of sample patients. Specifically, the primary filtering (642) may be performed by removing from the tumor tissue variant (630) a variant detected in the sequencing data for normal blood samples of the plurality of sample patients among the detected tumor tissue variants (630). That is, the variant detected from the normal blood samples of the plurality of sample patients may be assumed to be an error rather than an actual variant in the tumor tissue, and thus such variant information may be removed during the primary filtering (642).

[0103] Additionally, secondary filtering (644) may be performed on the tumor tissue mutations subjected to the primary filtering (642). The secondary filtering (644) may be performed using sequencing data for plasma samples of a plurality of sample patients. Specifically, among the detected tumor tissue mutations, mutations detected in the sequencing data for the plasma samples of the plurality of sample patients may be removed from the tumor tissue mutation information subjected to the primary filtering (642). That is, mutations that match mutations detected in the sequencing data for the plasma samples of the plurality of sample patients may be assumed to be errors in the system and removed from the tumor tissue mutations subjected to the primary filtering (642).

[0104] Although FIG. 6 illustrates and describes that the first filtering (642) is performed followed by the second filtering (644), this is not a limitation. For example, the second filtering (644) described above may be performed first, followed by the first filtering (642) described above. In another example, either the first filtering (642) or the second filtering (644) may be omitted.

[0105] Meanwhile, the average sequencing depth (670) of the ctDNA or plasma sequencing data (660) in the blood of the target patient can be calculated from the plasma sequencing data (660). Thereafter, a detection limit (680) can be calculated based on the filtered tumor tissue mutation information (650) and the average sequencing depth (670) of the plasma sequencing data (660). For example, the detection limit (680) can be calculated based on the following mathematical equation (1).

[0106]

[0107] Here, N represents the number of mutations detected in tumor tissue (e.g., the number of filtered tumor tissue mutations), and cov may represent the average sequencing depth of plasma sequencing data.

[0108] FIG. 7 is a diagram illustrating a tumor detection process (780) according to one embodiment of the present disclosure. As illustrated, a tumor cell fraction (TCF) (730) for a target patient can be calculated based on plasma sequencing data (710) and a reference sequence (720). Additionally, a corrected tumor cell fraction (760) can be calculated using a random error rate (750). Thereafter, tumor detection (780) can be performed based on the corrected tumor cell fraction (760) and the patient's limit of detection (LOD) (770). At this time, the LOD (770) may be the target patient's limit of detection (680) calculated in FIG. 6 .

[0109] In one embodiment, the tumor cell ratio (730) may be calculated based on plasma sequencing data (710) and a reference sequence (720). Specifically, the number of reads that differ from the reference sequence (720) and the number of reads that match the reference sequence (720) in the plasma sequencing data (710) may be determined, respectively. Thereafter, the tumor cell ratio (730) may be calculated based on the number of reads that differ from the reference sequence (720) and the number of reads that match the reference sequence (720). For example, the tumor cell ratio (730) may be calculated by the following mathematical equation (2).

[0110]

[0111] Here, Tumor Cell Fraction (raw) is the tumor cell ratio (730), is the number of reads different from the reference sequence (720), represents the number of reads matching the reference sequence (720).

[0112] Alternatively, the tumor cell ratio (730) can be calculated based on the filtered tumor tissue variants (650) of FIG. 6. Since the filtered tumor tissue variants (650) information of FIG. 6 includes information associated with regions where variants occur in the genome of tumor cells, the number of reads containing variants detected in tumor tissue can be calculated using the filtered tumor tissue variants.

[0113] Specifically, based on the filtered tumor tissue mutation information, the number of reads containing mutations detected in the tumor tissue from the patient's plasma sequencing data is determined, and the determined number of reads is expressed by Equation 2. can be used. For example, when the number of filtered tumor tissue mutations is 2,000, the regions corresponding to the 2,000 tumor tissue mutations in the plasma sequencing data can be checked to determine whether a mutation exists. For example, when ctDNA in plasma is sequenced at 20x, the number of reads including a mutation detected in the tumor tissue can be determined among 20*2,000=40,000 reads. The number of reads including a mutation detected in the tumor tissue in the plasma sequencing data, which is determined using the filtered tumor tissue mutation (650) information of FIG. 6, can be substantially the same as or very close to the number of reads that are different from the reference sequence (720). This can reduce the time and computing resources required to compare the entire plasma sequencing data (710) with the reference sequence (720).

[0114] Meanwhile, the random error rate (750) can be calculated based on genetic data and reference sequences (720) associated with multiple sample patients distinct from the target patient. The genetic data associated with multiple sample patients can be obtained from a genetic database (740). Here, the genetic database (740) may be the same database as the genetic database (640) of FIG. 6 .

[0115] Genetic data associated with a plurality of sample patients included in the genetic database (740) may include plasma sequencing data of the plurality of sample patients. In one embodiment, the number of reads that differ from the reference sequence (720) and the number of reads that match the reference sequence (720) in the plasma sequencing data of the plurality of sample patients are determined, and then a random error rate (750) may be calculated based on each read count. For example, the random error rate (750) may be calculated based on the following mathematical equation (3).

[0116]

[0117] Here, Random Error Rate is the random error rate (750), is the number of reads that differ from the reference sequence (720) in the plasma sequencing data of multiple sample patients. represents the number of reads matching the reference sequence (720) in the plasma sequencing data of multiple sample patients.

[0118] Thereafter, the tumor cell ratio (730) can be corrected using the random error rate (750). For example, the corrected tumor cell ratio (760) can be a value obtained by subtracting the random error rate (750) from the tumor cell ratio (730).

[0119] Then, tumor detection (780) of the target patient (e.g., determining whether a tumor has recurred, etc.) can be performed based on the corrected tumor cell ratio (760) and the detection threshold (770). For example, a confidence interval with a predetermined confidence level can be calculated for the corrected tumor cell ratio (760). For example, when a 95% confidence level is used, the confidence interval at the 95% confidence level can be calculated using the following mathematical expression (4).

[0120]

[0121] Here, TCF represents the corrected tumor cell ratio (760).

[0122] Thereafter, if the lower limit of the confidence interval of the calculated corrected tumor cell ratio (760) is determined to be higher than the detection limit (770), the patient's tumor may be determined to have recurred (tumor detection (780)). If the patient's tumor is determined to have recurred, tumor treatment / surgery may be performed on the subject patient.

[0123] Unlike what is illustrated and described in FIG. 7, tumor detection (780) can be performed using structural variants. In one embodiment, whether a subject's tumor has recurred can be determined based on a comparison of a first set of structural variants detected from tumor tissue sequencing data and a second set of structural variants detected from plasma sequencing data. For example, if the number or ratio of structural variants included in the second set of structural variants among the first set of structural variants is greater than a predetermined threshold, the patient can be determined to have recurred a tumor. For example, the predetermined threshold can be 0.9. In this case, the first set of structural variants can include at least one of an inversion, a translocation, a duplication, a deletion, or an insertion in a certain region of the genome.

[0124] FIG. 8 is a diagram illustrating examples of graphs (810, 820) illustrating the relationship between the number of mutations detected in tumor cells of a target patient and the detection threshold of the target patient according to one embodiment of the present disclosure. The graphs (810, 820) illustrate actual measurement results of the detection threshold of each sample patient based on ctDNA samples from 33 sample patients. Specifically, the first graph (810) represents the detection threshold of all 33 sample patients, and the second graph (820) represents the detection threshold within a specific range of the first graph (810).

[0125] In the first graph (810), the detection limit of the entire sample patient ranges from about 1.48E-06 to 1.49E-03, and the median is about 1.99E-05. The detection limit of 29 of the 33 sample patients (about 87% of the sample patients) is less than 1E-4. For the sample patient in which the ctDNA matching the tumor cells with more than 20,000 mutations is detected, the detection limit is lowered to 1E-6. In other words, the detection limit of the sample patient is correlated with the number of mutations (Somatic Variant Count) of the tumor cells of the sample patient, and as the number of mutations of the tumor cells increases, the detection limit, which is the lowest tumor cell ratio that can be detected in the plasma sample of the tumor patient, tends to be lower, so that tumor detection can be facilitated.

[0126] FIG. 9 is a diagram illustrating an example of generating arm-level copy number profiles (912, 922, 932) according to one embodiment of the present disclosure. Additionally or alternatively to the methods described above in FIGS. 6 and 7, the arm-level copy number profiles can be used to detect residual microscopic disease.

[0127] In one embodiment, a copy number profile (912) of a first chromosome arm may be generated from tumor tissue sequencing data (910) of a target patient, a copy number profile (922) of a second chromosome arm may be generated from normal blood sequencing data (920), and a copy number profile (932) of a third chromosome arm may be generated from plasma sequencing data (930). At this time, the tumor tissue sequencing data (910), the normal blood sequencing data (920), and the plasma sequencing data (930) may be obtained from a tumor tissue biopsy sample, a normal blood sample, and a plasma sample, respectively, of the target patient.

[0128] In one embodiment, the arm-level copy number profiles (912, 922, 932) can be generated by mapping each of the sequencing data (910, 920, 930) to a reference genome. For example, for each of the sequencing data (910, 920, 930), genomic position information of the chromosome arm can be obtained, sequencing coverage (or depth or number of reads) of reads mapped to each chromosome arm can be counted, and then normalized by dividing the total genome sequencing coverage of the WGS data, thereby obtaining a normalized chromosome arm-level copy number profile (912, 922, 932).

[0129] FIG. 10 is a diagram showing an example of calculating the tumor cell ratio of a target patient using copy number profiles (1010, 1020, 1030) at the chromosome arm level according to one embodiment of the present disclosure. The copy number profile (1030) of the third chromosome arm generated from the plasma sequencing data of the target patient may be considered as a mixture of the copy number profile (1010) of the first chromosome arm generated from the tumor tissue sequencing data of the target patient and the copy number profile (1020) of the second chromosome arm generated from the normal blood sequencing data of the target patient in a specific ratio. For example, as illustrated, the copy number profile (1010) of the first chromosome arm and the copy number profile (1020) of the second chromosome arm may be considered as a mixture in the ratio a:(1-a), where a is a real number greater than or equal to 0 and less than or equal to 1. Here, a is the tumor cell fraction (TCF) for the patient (e.g., 730 in Figure 7). The value of a (i.e., the tumor cell fraction) can be calculated using the non-negative least squares (NNLS) method expressed in Equation 5 below.

[0130]

[0131] Here, represents a Euclidian norm, A may be a matrix associated with the copy number profile (1010) of the first chromosome arm unit and the copy number profile (1020) of the second chromosome arm unit, and y may be a vector associated with the copy number profile (1030) of the third chromosome arm unit.

[0132] FIG. 11 is a diagram showing the results of performance verification of a method for detecting minimal residual disease of a target patient using genetic data of a sample patient according to one embodiment of the present disclosure. The first graph (1110) shows the results of verification of how accurately a residual disease detection method using genetic data of a sample patient (e.g., the method illustrated and described in FIGS. 6 and 7) can measure the tumor cell fraction (TCF) by mixing tumor tissue sequencing data and normal blood sequencing data with known tumor cell ratios in various ratios (in silico mixture). The second graph (1120) shows the results of verification of how accurately a residual disease detection method using sample patient data can measure the tumor cell fraction by mixing actual tumor tissue DNA with normal DNA (experimental mixture).

[0133] As shown in the first graph (1110) and the second graph (1120), in both cases where sequencing data was mixed and DNA was mixed, a correlation of over 99.9% was confirmed between the correct tumor cell ratio (Answer) and the predicted tumor cell ratio (Predicted) according to the present disclosure. In other words, it can be confirmed that the tumor cell ratio within the target patient's body can be predicted with high accuracy as a result of filtering out tumor tissue mutations in the target patient using the genetic data of the sample patient.

[0134] FIG. 12 is a diagram showing the performance verification results of a method for detecting minimal residual disease in a target patient using a copy number profile at the chromosome arm level of the target patient according to one embodiment of the present disclosure. As in FIG. 11, the performance verification of a method for detecting minimal residual disease in a target patient using a copy number profile at the chromosome arm level of the target patient (e.g., the method illustrated and described in FIGS. 9 and 10) was performed for an in silico mixture (first graph (1210)) and an experimental mixture (second graph (1220)).

[0135] As shown in the first graph (1210) and the second graph (1220), in both cases where sequencing data was mixed or DNA was mixed, a correlation of over 99.9% was confirmed between the correct tumor cell ratio (Answer) and the predicted tumor cell ratio (Predicted) according to the present disclosure. In other words, it can be confirmed that the tumor cell ratio within the subject patient's body can be predicted with high accuracy using the copy number profile at the chromosome arm level of the subject patient.

[0136] FIG. 13 is a flowchart illustrating a method (1300) for detecting minimal residual disease using tumor information according to one embodiment of the present disclosure. The method (1300) may be performed by at least one processor of a system for detecting minimal residual disease using tumor information. The method (1300) may begin when the processor acquires first sequencing data associated with a first sample of a patient (S1310). In this case, the first sample may be a tumor tissue biopsy sample of the patient, and the first sequencing data may be acquired through whole genome sequencing (WGS).

[0137] Thereafter, the processor may obtain second sequencing data associated with the patient's second sample (S1320). At this time, the second sample may be a normal blood sample of the patient, and the second sequencing data may be obtained through whole genome sequencing.

[0138] Thereafter, the processor may acquire third sequencing data associated with the patient's third sample (S1330). At this time, the third sample is the patient's plasma sample, and the plasma sample may include cell-free deoxyribonucleic acid (cfDNA) and circulating tumor deoxyribonucleic acid (ctDNA). The third sequencing data may be acquired through whole genome sequencing.

[0139] In one embodiment, the first sample and the second sample are samples obtained at a first time point, the third sample is a sample obtained at a second time point after the first time point, and at least one of surgery or treatment may be performed on the patient after the first time point and before the second time point.

[0140] The processor may compare the first sequencing data and the second sequencing data to detect tumor tissue mutation information of the patient, and perform background error filtering on the detected tumor tissue mutation information using genetic data associated with a plurality of sample patients that are distinct from the patient. In this case, the genetic data may include sequencing data for normal blood samples of the plurality of sample patients and / or sequencing data for plasma samples of the plurality of sample patients.

[0141] In one embodiment, the processor may perform background error filtering by removing, from the tumor tissue variant information, variants detected in the sequencing data for normal blood samples of a plurality of sample patients among the detected tumor tissue variants. In another embodiment, the processor may perform background error filtering by removing, from the tumor tissue variant information, variants detected in the sequencing data for plasma samples of a plurality of sample patients among the detected tumor tissue variants.

[0142] Thereafter, the processor may perform detection of minimal residual disease for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data (S1340). In one embodiment, the processor may calculate a Limit of Detection (LOD) value for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data, calculate a tumor cell fraction (TCF) for the patient based on the third sequencing data, correct the tumor cell fraction using genetic data associated with a plurality of sample patients that are distinct from the patient, and determine whether the patient's tumor has recurred based on the corrected tumor cell fraction and the LOD value, thereby performing detection of minimal residual disease for the patient. At this time, the LOD value may represent the lowest tumor cell fraction that can be detected in a plasma sample of the patient. Additionally, the detection limit can be calculated based on the number of tumor tissue mutations detected in the patient by comparing the first sequencing data and the second sequencing data and the average sequencing depth of the third sequencing data.

[0143] In one embodiment, the processor may determine the number of reads that are different from a reference sequence in the third sequencing data, determine the number of reads that match the reference sequence in the third sequencing data, and calculate the tumor cell ratio based on the number of reads that are different from the reference sequence and the number of reads that match the reference sequence. At this time, the processor may compare the first sequencing data and the second sequencing data to detect tumor tissue mutation information of the patient, determine the number of reads that include a mutation detected in the tumor tissue in the third sequencing data based on the tumor tissue mutation information, and use the number of reads that include a mutation detected in the tumor tissue in the third sequencing data as the number of reads that are different from the reference sequence.

[0144] In one embodiment, the processor determines a number of reads that are different from a reference sequence in plasma sequencing data included in genetic data associated with a plurality of sample patients that are distinct from the patient, determines a number of reads that match a reference sequence in plasma sequencing data included in genetic data associated with a plurality of sample patients that are distinct from the patient, and calculates a random error rate based on the number of reads that are different from the reference sequence and the number of reads that match the reference sequence, and corrects a tumor cell ratio using the random error rate.

[0145] In one embodiment, the processor can determine that the patient's tumor has recurred in response to determining that a confidence interval of a predetermined confidence level of the corrected tumor cell ratio is generated, and a lower limit of the generated confidence interval is higher than a generated detection threshold.

[0146] In one embodiment, the processor can identify a first set of structural variants detected from the first sequencing data, identify a second set of structural variants detected from the third sequencing data, and determine whether the patient has a recurrence of a tumor based on a comparison result between the first set of structural variants and the second set of structural variants. For example, the processor can determine that the patient has a recurrence of a tumor if the number or ratio of structural variants included in the second set of structural variants among the first set of structural variants is greater than or equal to a threshold. In this case, the first set of structural variants can include at least one of an inversion, a translocation, a duplication, a deletion, or an insertion in a region of a genome.

[0147] Alternatively, the processor may perform minimal residual disease detection by generating a chromosomal arm-level copy number profile of a first chromosomal arm based on the first sequencing data, generating a chromosomal arm-level copy number profile of a second chromosomal arm based on the second sequencing data, generating a chromosomal arm-level copy number profile of a third chromosomal arm based on the third sequencing data, and calculating a tumor cell ratio based on the copy number profiles of the first to third chromosomal arms.

[0148] The flowchart illustrated in Figure 13 and the description above are merely examples, and some embodiments may be implemented differently. For example, one or more steps may be omitted, the order of each step may be changed, one or more steps may be performed in an overlapping manner, or one or more steps may be repeated multiple times.

[0149] The above-described method may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may be one that continuously stores a computer-executable program or one that temporarily stores it for execution or download. In addition, the medium may be various recording or storage means in the form of a single or multiple hardware combinations, and is not limited to a medium directly connected to a computer system, but may also be distributed over a network. Examples of the medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and those configured to store program instructions, including ROM, RAM, and flash memory. In addition, examples of other media may include recording or storage media managed by app stores that distribute applications, sites that supply or distribute various software, servers, etc.

[0150] The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will appreciate that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design requirements imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementations should not be construed as departing from the scope of the present disclosure.

[0151] In a hardware implementation, the processing units used to perform the techniques may be implemented within one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, a computer, or a combination thereof.

[0152] Accordingly, the various exemplary logical blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0153] In a firmware and / or software implementation, the techniques may be implemented as instructions stored on a computer-readable medium, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, a compact disc (CD), a magnetic or optical data storage device, etc. The instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described herein.

[0154] While the embodiments described above have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or distributed computing environment. Furthermore, aspects of the present disclosure may be implemented in multiple processing chips or devices, and storage may be similarly affected across multiple devices. Such devices may include personal computers, network servers, and portable devices.

[0155] While the present disclosure has been described in connection with certain embodiments herein, various modifications and variations may be made without departing from the scope of the present disclosure, which would be apparent to those skilled in the art. Furthermore, such modifications and variations are intended to fall within the scope of the claims appended to this specification.

Claims

1. A method for detecting minimal residual disease performed by at least one processor, A step of obtaining first sequencing data associated with a first sample of a patient; A step of obtaining second sequencing data associated with a second sample of the patient; A step of obtaining third sequencing data associated with a third sample of the patient; and A step of performing detection of minimal residual disease for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data. A method for detecting residual microscopic disease, comprising:

2. In paragraph 1, A method for detecting a minimal residual disease, wherein the first sequencing data, the second sequencing data, and the third sequencing data are obtained through whole genome sequencing (WGS).

3. In paragraph 1, The first sample above is a tumor tissue biopsy sample of the patient, The second sample is a normal blood sample of the patient, The third sample above is a plasma sample of the patient, A method for detecting minimal residual disease, wherein the plasma sample contains cfDNA (cell free Deoxyribo Nucleic Acid) and ctDNA (circulating tumor Deoxyribo Nucleic Acid).

4. In paragraph 1, The above first sample and the above second sample are samples obtained at the first time point, The above third sample is a sample obtained at a second time point after the above first time point, A method for detecting minimal residual disease, wherein at least one of surgery or treatment is performed on the patient after the first time point and before the second time point.

5. In paragraph 1, A step of detecting tumor tissue mutation information of the patient by comparing the first sequencing data and the second sequencing data; and A step of performing background error filtering on the detected tumor tissue mutation information using genetic data associated with a plurality of sample patients that are distinct from the above patient. A method for detecting residual microscopic disease, further comprising:

6. In paragraph 5, The genetic data includes sequencing data for normal blood samples of the plurality of sample patients, The steps for performing the above filtering are: A step of removing mutations detected in sequencing data for normal blood samples of the plurality of sample patients from the tumor tissue mutation information among the detected tumor tissue mutations. A method for detecting residual microscopic disease, comprising:

7. In paragraph 5, The genetic data includes sequencing data for plasma samples of the plurality of sample patients, The steps for performing the above filtering are: A step of removing a mutation detected in sequencing data for plasma samples of the plurality of sample patients from the tumor tissue mutation information among the detected tumor tissue mutations. A method for detecting residual microscopic disease, comprising:

8. In paragraph 1, The step of performing the above-mentioned microscopic residual disease detection is as follows: A step of calculating a limit of detection value for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data; A step of calculating a tumor cell fraction (TCF) for the patient based on the third sequencing data; A step of correcting the tumor cell ratio using genetic data associated with a plurality of sample patients that are distinct from the patient; and A step of determining whether the patient's tumor has recurred based on the above-mentioned corrected tumor cell ratio and the above-mentioned detection limit. A method for detecting residual microscopic disease, comprising:

9. In paragraph 8, A method for detecting minimal residual disease, wherein the limit of detection (LOD) value represents the lowest tumor cell percentage that can be detected in the patient's plasma sample.

10. In paragraph 8, A method for detecting minimal residual disease, wherein the detection limit is calculated based on the number of tumor tissue mutations of the patient detected by comparing the first sequencing data and the second sequencing data and the average sequencing depth of the third sequencing data.

11. In paragraph 8, The step of calculating the tumor cell ratio is as follows: A step of determining the number of reads that are different from the reference sequence in the third sequencing data; A step of determining the number of reads matching the reference sequence in the third sequencing data; and A step of calculating the tumor cell ratio based on the number of reads different from the reference sequence and the number of reads matching the reference sequence. A method for detecting residual microscopic disease, comprising:

12. In paragraph 11, The step of determining the number of different leads is: A step of detecting tumor tissue mutation information of the patient by comparing the first sequencing data and the second sequencing data; A step of determining the number of reads including a mutation detected in tumor tissue in the third sequencing data based on the tumor tissue mutation information; and A step of using the number of reads including mutations detected in tumor tissue from the third sequencing data as a number of reads different from the reference sequence. A method for detecting residual microscopic disease, comprising:

13. In paragraph 8, The step of correcting the tumor cell ratio is: A step of determining the number of reads that are different from a reference sequence in plasma sequencing data included in genetic data associated with a plurality of sample patients that are distinct from the patient; A step of determining the number of reads matching a reference sequence in plasma sequencing data included in genetic data associated with a plurality of sample patients distinct from the patient; A step of calculating a random error rate based on the number of reads that are different from the reference sequence and the number of reads that match the reference sequence; and A step of correcting the tumor cell ratio using the random error rate. A method for detecting residual microscopic disease, comprising:

14. In paragraph 8, The step of determining whether the patient's tumor has recurred is as follows: A step of calculating a confidence interval of a predetermined confidence level of the above-mentioned corrected tumor cell ratio; and A step of determining that the patient's tumor has recurred in response to determining that the lower limit of the confidence interval calculated above is higher than the detection limit calculated above. A method for detecting residual microscopic disease, comprising:

15. In paragraph 1, The step of performing the above-mentioned microscopic residual disease detection is as follows: A step of generating a chromosomal arm-level copy number profile based on the first sequencing data; A step of generating a copy number profile of a second chromosome arm unit based on the second sequencing data; A step of generating a copy number profile of a third chromosome arm unit based on the third sequencing data; and A step of calculating the tumor cell ratio based on the copy number profile of the first to third chromosome arms. A method for detecting residual microscopic disease, further comprising:

16. In paragraph 3, The step of performing the above-mentioned microscopic residual disease detection is as follows: A step of identifying a first set of structural variants detected from the first sequencing data; A step of identifying a second set of structural variants detected from the third sequencing data; and A step of determining whether the patient's tumor relapses based on the comparison results of the first set of structural mutations and the second set of structural mutations. A method for detecting residual microscopic disease, further comprising:

17. In paragraph 16, The step of determining whether the patient's tumor has recurred is as follows: A step of determining that the tumor has recurred in the patient when the number or ratio of structural mutations included in the second set of structural mutations among the first set of structural mutations is greater than or equal to a threshold value. A method for detecting residual microscopic disease, comprising:

18. In paragraph 16, A method for detecting minimal residual disease, wherein the structural variation of the first set comprises at least one of inversion, translocation, duplication, deletion or insertion of a region of the genome.

19. A computer-readable non-transitory recording medium recording commands for executing the method according to paragraph 1 on a computer.

20. As a system, Communication module; memory; and At least one processor connected to said memory and configured to execute at least one computer-readable program contained in said memory, At least one program above, Obtaining first sequencing data associated with the patient's first sample, Obtaining second sequencing data associated with the second sample of the patient; Obtain third sequencing data associated with the third sample of the above patient, A system comprising commands for performing detection of minimal residual disease for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data.

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