JP-2026514368-A - Method and apparatus for detecting minimal residual disease using tumor information

Abstract

[Problem] This disclosure relates to a method for detecting minimal residual disease using tumor information. [Solution] A minimal residual disease detection method utilizing tumor information includes the steps of: acquiring first sequencing data associated with a first sample of the patient; acquiring second sequencing data associated with a second sample of the patient; acquiring 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. [Selection Diagram] Figure 1

Inventors

• ジュ ヨンソク
• イ ウォンチョル

Assignees

• イノクラス コリア・インコーポレイテッド

Dates

Publication Date

20260511

Application Date

20231108

Priority Date

20230822

Claims (20)

1. A method for detecting minimal residual disease, which is performed by at least one processor, A step of obtaining first sequencing data associated with the first sample of the patient, The steps include obtaining second sequencing data associated with a second sample of the aforementioned patient, The steps include obtaining third sequencing data associated with the third sample of the aforementioned patient, A step of performing minimal residual disease detection for the patient based on the first sequencing data, the second sequencing data, and the third sequencing data, A method for detecting minimal residual lesions, including the above.
2. The method for detecting minimal residual disease according to claim 1, wherein the first sequencing data, the second sequencing data, and the third sequencing data are obtained via whole-genome sequencing (WGS).
3. The first sample is a tumor tissue biopsy sample from the patient. The second sample is a normal blood sample from the patient. The third sample is a plasma sample from the patient. The method for detecting minimal residual disease according to claim 1, wherein the plasma sample comprises cfDNA (cell-free Deoxyribo Nucleic Acid) and ctDNA (circulating tumor Deoxyribo Nucleic Acid).
4. The first and second samples are samples acquired at the first time point. The third sample is a sample acquired at a second time point after the first time point. The method for detecting minimal residual disease according to claim 1, wherein at least one of surgery or therapeutic treatment is performed on the patient after the first time point and before the second time point.
5. A step of comparing the first sequencing data and the second sequencing data to detect tumor tissue mutation information of the patient, The steps include: performing background error filtering on the detected tumor tissue mutation information using genetic data associated with multiple sample patients that are distinguished from the aforementioned patient; The method for detecting minute residual lesions according to claim 1, further comprising:
6. The genetic data includes sequencing data for normal blood samples from the plurality of sample patients. The step of performing the aforementioned filtering is: The method for detecting minimal residual disease according to claim 5, further comprising the step of removing from the tumor tissue mutation information mutations detected in sequencing data for normal blood samples of the plurality of sample patients among the detected tumor tissue mutations.
7. The genetic data includes sequencing data for plasma samples from the plurality of sample patients. The step of performing the aforementioned filtering is: The method for detecting minimal residual disease according to claim 5, further comprising the step of removing from the tumor tissue mutation information mutations that are detected in sequencing data for plasma samples of the plurality of sample patients among the detected tumor tissue mutations.
8. The step of performing the detection of minute residual lesions is, 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 the tumor cell fraction (TCF) for the patient based on the third sequencing data, The steps include correcting the tumor cell ratio using genetic data associated with multiple sample patients distinct from the aforementioned patient, A step of determining whether the patient's tumor is recurrent based on the corrected tumor cell ratio and the detection limit, A method for detecting minute residual lesions according to claim 1, including the method described in claim 1.
9. The method for detecting minimal residual disease according to claim 8, wherein the Limit of Detection value refers to the lowest tumor cell percentage detectable in the patient's plasma sample.
10. The method for detecting minimal residual disease according to claim 8, wherein the detection limit is calculated based on the number of tumor tissue mutations in 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. The step of calculating the tumor cell ratio is: The third sequencing data includes a step of determining the number of reads that differ from the reference sequence, The third step of determining the number of reads in the sequencing data that match the reference sequence, A step of calculating the tumor cell ratio based on the number of reads that differ from the reference sequence and the number of reads that match the reference sequence, A method for detecting minute residual lesions according to claim 8, including the method described in claim 8.
12. The step of determining the number of differing leads is: A step of comparing the first sequencing data and the second sequencing data to detect tumor tissue mutation information of the patient, The steps include determining the number of reads containing mutations detected in tumor tissue in the third sequencing data based on the tumor tissue mutation information, The third step of using the number of reads containing mutations detected in tumor tissue in the third sequencing data as the number of reads that differ from the reference sequence, A method for detecting minute residual lesions according to claim 11, including the method described in claim 11.
13. The step of correcting the tumor cell ratio is: The steps include determining the number of reads that differ from the reference sequence in plasma sequencing data included in the genetic data associated with multiple sample patients distinct from the aforementioned patient, The steps include determining the number of reads that match a reference sequence in plasma sequencing data included in the genetic data associated with multiple sample patients distinct from the aforementioned patient, A step of calculating the random error rate based on the number of reads that differ from the reference sequence and the number of reads that match the reference sequence, A step of correcting the tumor cell ratio using the aforementioned random error rate, A method for detecting minute residual lesions according to claim 8, including the method described in claim 8.
14. The step of determining whether the tumor in the aforementioned patient is likely to recur is: The steps include: calculating the confidence interval of a predetermined confidence level for the corrected tumor cell ratio; In response to determining that the lower limit of the calculated confidence interval is higher than the calculated detection limit, the step of determining that the patient's tumor has recurred, A method for detecting minute residual lesions according to claim 8, including the method described in claim 8.
15. The step of performing the detection of minute residual lesions is, The steps include generating a chromosomal arm-level copy number profile based on the first sequencing data, The steps include generating a copy number profile at the chromosome 2 arm level based on the second sequencing data, The steps include generating a copy number profile at the third chromosome arm level based on the third sequencing data, A step of calculating the tumor cell ratio based on the copy number profiles at the level of the first to third chromosome arms, The method for detecting minute residual lesions according to claim 1, further comprising:
16. The step of performing the detection of minute residual lesions is, The steps include identifying a first set of structural mutations detected from the first sequencing data, The steps include identifying a second set of structural mutations detected from the third sequencing data, A step of determining whether the patient's tumor is likely to recur based on the results of comparing the first set of structural mutations with the second set of structural mutations, The method for detecting minute residual lesions according to claim 3, further comprising:
17. The step of determining whether the tumor in the aforementioned patient is likely to recur is: A method for detecting minimal residual disease according to claim 16, comprising the step of determining that the tumor has recurred in the patient if the number or ratio of structural mutations included in the second set of structural mutations among the first set of structural mutations is equal to or greater than a threshold.
18. The method for detecting minimal residual disease according to claim 16, wherein the first set of structural mutations includes at least one of the following: inversion, translocation, duplication, deletion, or insertion of a region of the genome.
19. A computer-readable non-temporary recording medium containing instructions for executing the method according to claim 1.
20. It is a system, Communication module and Memory and A processor connected to the memory and configured to execute at least one computer-readable program contained in the memory, Includes, The aforementioned at least one program, First sequencing data associated with the first sample of the patient was obtained. Second sequencing data related to the second sample of the aforementioned patient was obtained. Third sequencing data associated with the third sample of the aforementioned patient is obtained, A system including commands for performing minimal residual disease detection on the patient based on the first sequencing data, the second sequencing data, and the third sequencing data.

Description

This disclosure relates to a method and apparatus for detecting minimal residual disease (MRP) using tumor information, specifically, a method and apparatus for performing MRP detection in patients using tumor tissue mutation information and liquid biopsy samples. Genetic analysis technology is widely used in the medical field, for example, to determine the characteristics or temperament of an organism by understanding its genes. Recently, medical practices for treating various diseases such as tumors have evolved from a traditional prescription-centered approach to precision medicine, that is, a form of personalized treatment that takes into account the individual patient's genetic information and health records. In the field of precision medicine, acquiring vast amounts of personal genetic information and performing related clinical analyses is crucial. Recently, the Next-Generation Sequencing (NGS) technique, which allows for the rapid parallel reading of large amounts of DNA information, has been widely used in various medical fields, including cancer screening. While NGS offers advantages such as reduced cost and time for genome analysis, it has an inherent limitation: a background error rate of approximately 0.1% to 1%. Specifically, even in DNA without mutations, the sequencing process can mistakenly identify one base pair out of 100 to 1,000 as having a mutation (i.e., an error rate of 0.1% to 1%) (False Positive Mutation). Due to these inherent limitations of NGS techniques, they have difficulty detecting DNA mutations present at very low concentrations. For example, cancer-derived DNA (e.g., ctDNA: Circulation tumor DNA) is typically present at concentrations of less than 1% in the blood of cancer patients. Therefore, it is difficult to distinguish whether mutations detected in blood using NGS techniques originate from cancer tissue or are false positives due to the background error rate. Consequently, there is a need for new techniques that can detect DNA mutations present at very low concentrations in the patient's blood (such as tumor cell residues) by reducing the background error rate. Korean Published Patent No. 10-2015-0017525 Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, where similar reference numbers indicate similar elements, but are not limited thereto. This figure illustrates an example of the process of performing minimal residual disease detection on a target patient according to one embodiment of the present disclosure. This is a schematic diagram showing a configuration in which an information processing system is connected to multiple user terminals in a communicative manner in order to provide a minimal residual disease detection service utilizing tumor information according to one embodiment of the present disclosure. This 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. This figure shows an example of a graph illustrating the percentage of tumor cells in a patient over time, according to one embodiment of the present disclosure. This is a diagram showing a blood sample from a target patient according to one embodiment of the present disclosure. This figure shows the process by which the detection limit is calculated for a target patient according to one embodiment of the present disclosure. This figure shows a tumor detection process according to one embodiment of the present disclosure. This figure shows an example graph illustrating the relationship between the number of mutations detected in tumor cells of a target patient and the detection limit of that patient, according to one embodiment of the present disclosure. This figure shows an example of how an arm-unit copy count profile is generated according to one embodiment of the present disclosure. This figure shows an example of how the percentage of tumor cells in a patient's body is calculated using a chromosome arm-level copy number profile according to one embodiment of the present disclosure. This figure shows the performance verification results of a method for detecting minimal residual disease in a target patient using the genetic data of a sample patient according to one embodiment of the present disclosure. This figure shows the performance verification results of a method for detecting minimal residual disease in a target patient using the chromosomal arm-level copy number profile of the target patient, according to one embodiment of the present disclosure. This flowchart shows a method for detecting minimal residual disease using tumor information according to one embodiment of the present disclosure. <Summary of the Invention> In one embodiment of the present disclosure, the first sequencing data, the second sequencing data, and the third sequencing data are obtained via whole genome sequencing (WGS). In one embodiment of thi