System and kit for early auxiliary diagnosis of breast cancer and detection of minimal residual lesions in various stages

Abstract

The invention discloses a system and a kit for early auxiliary diagnosis of breast cancer and detection of minimal residual lesions in various stages, and relates to the technical field of medical diagnosis. According to the system provided by the invention, the read value ratio of the miRNA mature body and the isomer of the miRNA mature body is utilized to carry out early-stage auxiliary diagnosis and tiny residual focus detection in each stage on the breast cancer, so that the system has relatively high diagnosis sensitivity and specificity; and a more accurate diagnosis basis is provided for treating or intervening breast cancer patients as soon as possible.

Description

Technical Field

[0001] This invention relates to the field of medical diagnostic technology, and more specifically, to a system and kit for the early auxiliary diagnosis of breast cancer and the detection of minimal residual disease at various stages. Background Technology

[0002] Breast cancer is currently the most common and prevalent malignant tumor among women worldwide, seriously threatening their lives and health. Early detection, accurate diagnosis, and real-time dynamic monitoring are key to improving the cure rate and prognosis of breast cancer. Currently, clinical management of breast cancer mainly relies on the following traditional techniques: Imaging examinations: such as mammography, ultrasound, and magnetic resonance imaging (MRI). These methods are important for early screening, but they still have limitations such as high false-positive rates (leading to unnecessary biopsies), low sensitivity to dense breast tissue, and inability to provide molecular biological information about the tumor. Tissue biopsy: As the "gold standard" for diagnosing breast cancer, it involves obtaining tumor tissue through puncture or surgery for pathological and molecular subtyping (such as ER, PR, HER2, Ki-67). More importantly, tumors exhibit spatial heterogeneity (different regions of the same tumor have different characteristics) and temporal heterogeneity (the tumor genome evolves during treatment), and a single tissue biopsy cannot comprehensively and dynamically reflect the overall picture and evolution of the tumor. Traditional tumor marker detection: such as CA15-3, CEA, etc. The concentration detection of these protein biomarkers in the blood is often used for monitoring treatment efficacy, but its sensitivity and specificity are both low, especially in early-stage breast cancer where the positive rate is extremely low, making it unsuitable for early diagnosis. With the rapid development of gene testing technology, liquid biopsy is playing an increasingly prominent role in precision oncology, providing a new direction for the comprehensive management of breast cancer. Liquid biopsy detects trace amounts of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating cell-free DNA (cfDNA), and small extracellular vesicle (sEV) contents in bodily fluids such as blood and urine, thereby enabling more precise comprehensive management of breast cancer patients and providing molecular-level information. Currently, ctDNA detection technology is the most widely used.

[0003] Despite its promising prospects, the clinical application of ctDNA testing in the comprehensive management of breast cancer still faces numerous challenges. Firstly, there is the challenge of sensitivity (especially in early-stage breast cancer and the follow-up stage of minimal residual disease (MRD)). Early-stage breast cancer or MRD patients have small tumors, and the amount of ctDNA released into the bloodstream from apoptosis or necrosis of cancer cells is extremely low, potentially below 0.01%. Even the most sensitive NGS testing technology can be affected by background noise that can mask these extremely weak signals, leading to false negatives—meaning that cancer cells are actually present in the patient's body but cannot be detected. Secondly, there are challenges related to specificity and false positives. Benign mutations in hematopoietic stem cells that occur with age can appear in white blood cells and plasma, easily misinterpreted as tumor origin. This can lead to false positives, potentially causing unnecessary anxiety and over-medical intervention for patients. Finally, for metastatic lesions that do not release ctDNA or release very little, NGS testing may result in an incomplete assessment of the tumor's genetic profile, affecting the accuracy of treatment decisions. Summary of the Invention

[0004] The purpose of this invention is to provide a system and kit for the early auxiliary diagnosis of breast cancer and the detection of minimal residual disease at various stages. The system provided by this invention can be used for the early auxiliary diagnosis of breast cancer and the detection of minimal residual disease, exhibiting high diagnostic sensitivity and specificity; it provides a more accurate diagnostic basis for breast cancer patients to adopt treatment or intervention methods as early as possible.

[0005] This invention is implemented as follows:

[0006] A system for early auxiliary diagnosis and / or detection of minimal residual disease in breast cancer, the system comprising an information acquisition module, a calculation module, and a diagnostic module;

[0007] The information acquisition module is used to perform the step of acquiring sequencing information of multiple mature miRNAs and their isoforms of the subject; the sequencing information includes sequencing reads values;

[0008] The calculation module is used to perform the step of substituting the sequencing reads value into the calculation model to calculate the threshold; the threshold is determined by the ratio of the reads value of the mature miRNA to its corresponding isoform.

[0009] The diagnostic module is used to perform the operation of judging the health status of the subject based on a threshold.

[0010] This invention, through creative labor, discovers that by utilizing the ratio of sequencing reads of multiple mature miRNAs to their isoforms, early auxiliary diagnosis of breast cancer and / or detection of minimal residual disease at various stages can be performed with high diagnostic sensitivity and specificity.

[0011] Optionally, in some embodiments of the present invention, the isoforms include: miRNA-1nt isoform and miRNA-2nt isoform.

[0012] Among them, the miRNA-2nt isoform refers to a sequence in which two nucleotides are deleted from the 3' end of the mature sequence of the target miRNA;

[0013] The miRNA-1nt isoform is a sequence in which one nucleotide is deleted from the 3' end of the mature sequence of the target miRNA.

[0014] Optionally, in some embodiments of the present invention, the computational model includes an early auxiliary diagnostic model for breast cancer;

[0015] The mathematical formula for the early auxiliary diagnostic model for breast cancer includes: S1=ratio1;

[0016] S1 represents the risk threshold for breast cancer in the subject; ratio1 represents the sum of the ratios of the sequencing reads of the 30 mature miRNAs to the sequencing reads of their respective miRNA-1nt isoforms.

[0017] The formula for calculating ratio1 is as follows:

[0018] ratio1=a1+a2+a3+a4+a5+a6+a7+a8+a9+a10+a11+a12+a13+a14+a15+a16+a17+a18+a19+a20+a21+a22+a23+a24+a25+a26+a27+a28+a29+a30;

[0019] Where a1-a30 represent the ratios of the mature read values ​​of 30 miRNAs to the sequencing read values ​​of their respective miRNA-1nt isoforms;

[0020] The 30 mature miRNAs used vary depending on the molecular subtype of breast cancer; details are as follows:

[0021] When used for the diagnosis of Luminal A breast cancer, the 30 mature miRNAs consist of the following 30 miRNAs: hsa-miR-100-5p, hsa-miR-106-3p, hsa-miR-10a-5p, hsa-miR-122-5p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-146b-5p, hsa-miR-148a-3p, hsa-miR-15a-5p, hsa-miR-182-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-19 9a-3p, hsa-miR-199b-3p, hsa-miR-223-3p, hsa-miR-26a-3p, hsa-miR-26b-5p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-320a-3p, hsa-miR-3 28-3p, hsa-miR-409-3p, hsa-miR-483-5p, hsa-miR-486-5p, hsa-miR-584-5p, hsa-miR-7-5p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p;

[0022] When used for the diagnosis of Luminal B breast cancer, the 30 mature miRNAs consist of the following 30 miRNAs: hsa-let-7f-5p, hsa-miR-100-5p, hsa-miR-10a-5p, hsa-miR-126-3p, hsa-miR-139-3p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-146b-5p, hsa-miR-15a-5p, hsa-miR-183-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199a -5p, hsa-miR-199b-3p, hsa-miR-223-3p, hsa-miR-25-3p, hsa-miR-26a-3p, hsa-miR-29a-3p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-30e-5 p, hsa-miR-320a-3p, hsa-miR-409-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-532-3p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p;

[0023] When diagnosing HER2-positive breast cancer, the 30 mature miRNAs described herein consist of the following 30 miRNAs:

[0024] hsa-let-7a-5p, hsa-let-7d-3p, hsa-miR-100-5p, hsa-miR-10a-5p, hsa-miR-127-3p, hsa-miR-129-5p, hsa-miR-1307-3p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-145-5p, hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199b-3p, hsa-miR-223-3p, hsa-miR-26a-3p, hsa-miR-26a-5p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-320a-3p, hsa-miR-375-3p, hsa-miR-409-3p, hsa-miR-483-5p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p;

[0025] When diagnosing triple-negative breast cancer, the 30 mature miRNAs are composed of the following 30 miRNAs: hsa-miR-100-5p, hsa-miR-10a-5p, hsa-miR-1287-5p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-146b-5p, hsa-miR-155-5p, hsa-miR-15a-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199b-3p, hsa-miR-19a-3p, hsa-miR-210-5p, hsa-miR-211-5p, hsa-miR-218-5p, hsa-miR-223-3p, hsa-miR-26a-3p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-3130-3p, hsa-miR-320a-3p, hsa-miR-338-3p, hsa-miR-34a-5p, hsa-miR-409-3p, hsa-miR-4485-3p, hsa-miR-483-5p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p.

[0026] Optionally, in some embodiments of the present invention, the diagnostic module makes a judgment based on the risk threshold S1 of the early auxiliary diagnostic model for breast cancer, and the judgment method is as follows:

[0027] If the subject's S1 score is ≤16.2, the subject is judged to be positive for breast cancer and the molecular subtype is Luminal A. If S1 is >16.2, the subject is judged to be negative or to have benign breast lesions.

[0028] If the subject's S1 score is ≤15.6, the subject is judged to be positive for breast cancer and the molecular subtype is Luminal B. If S1 is >15.6, the subject is judged to be negative or to have benign breast lesions.

[0029] If the subject's S1 score is ≤14.8, the subject is judged to be breast cancer positive and the molecular subtype is HER2 positive; if S1 is >14.8, the subject is judged to be negative or benign breast lesion.

[0030] If the subject's S1 score is ≤15.5, the subject is judged to be positive for breast cancer and the molecular subtype is triple-negative breast cancer (TNBC). If S1 is >15.5, the subject is judged to be negative or benign breast lesions.

[0031] Breast cancer has multiple molecular subtypes, and the treatment plans differ for each subtype. These differences in treatment can lead to variations in ctDNA gene mutations in breast cancer MRD testing. Using positive mutations from a baseline testing protocol for dynamic monitoring of breast cancer can result in a certain degree of false negatives.

[0032] The system provided by this invention can perform independent auxiliary diagnosis for different molecular subtypes, and each subtype has high specificity and sensitivity.

[0033] Optionally, in some embodiments of the present invention, the computational model includes a minimal residual lesion assessment model;

[0034] S² = ratio² + ratio³;

[0035] S2 represents the risk threshold for the subject having minimal residual disease in breast cancer;

[0036] ratio2 represents the sum of the ratios of the sequencing reads of 50 target miRNA matures to the sequencing reads of their respective miRNA-1nt isoforms, wherein the 50 target miRNA matures are composed of the top 50 miRNA matures whose sequencing reads of their miRNA-1nt isoforms are arranged from largest to smallest.

[0037] ratio3 represents the sum of the ratios of the sequencing reads of 50 target miRNA matures to the sequencing reads of their respective miRNA-2nt isoforms, wherein the 50 target miRNA matures are composed of the top 50 miRNA matures whose sequencing reads of their miRNA-2nt isoforms are arranged from largest to smallest.

[0038] The formula for calculating ratio2 is as follows:

[0039] ratio2=b1+b2+b3+b4+b5+b6+b7+b8+b9+b10+b11+b12+b13+b14+b15+b16+b17+b18+b19+b20+b21+b22+b23+b24+b25+b 26+b27+b28+b29+b30+b31+b32+b33+b34+b35+b36+b37+b38+b39+b40+b41+b42+b43+b44+b45+b46+b47+b48+b49+b50;

[0040] Here, b1-b50 represent the ratio of the read values ​​of 50 mature miRNAs to the sequencing read values ​​of their respective miRNA-1nt isoforms; the 50 mature miRNAs are composed of the top 50 mature miRNAs whose sequencing read values ​​of their miRNA-1nt isoforms are arranged from largest to smallest.

[0041] The formula for calculating ratio3 is as follows:

[0042] ratio3=c1+c2+c3+c4+c5+c6+c7+c8+c9+c10+c11+c12+c13+c14+c15+c16+c17+c18+c19+c20+c21+c22+c23+c24+c25+c 26+c27+c28+c29+c30+c31+c32+c33+c34+c35+c36+c37+c38+c39+c40+c41+c42+c43+c44+c45+c46+c47+c48+c49+c50;

[0043] Wherein, c1-c50 represent the ratio of the read values ​​of 50 mature miRNAs to the sequencing read values ​​of their respective miRNA-2nt isoforms; the 50 mature miRNAs are composed of the top 50 mature miRNAs whose sequencing read values ​​of their miRNA-2nt isoforms are arranged from largest to smallest.

[0044] It should be noted that when the system of the present invention is used for the diagnosis of different subjects, the mature miRNA sequences of the first 50 sequencing reads of different subjects may be completely identical or partially identical.

[0045] Optionally, in some embodiments of the present invention, the diagnostic module makes a judgment based on the risk threshold S2 of the residual microlesion assessment model, and the judgment method is as follows:

[0046] If S2 < 20.6, it is determined that there are residual breast cancer lesions in the patient, and the MRD result is positive.

[0047] If S2 > 48.9, it is determined that there are no residual breast cancer lesions in the subject's body, and the MRD result is negative;

[0048] If 20.6≤S2≤48.9, it is recommended that the subject be retested within 3 months or undergo enhanced clinical follow-up.

[0049] Breast cancer has multiple molecular subtypes, and the treatment plans differ for each subtype. These differences in treatment can lead to variations in ctDNA gene mutations in breast cancer MRD testing. Using positive mutations from a baseline testing protocol for dynamic monitoring of breast cancer can result in a certain degree of false negatives.

[0050] The system provided by this invention monitors or predicts minimal residual lesions in patients after surgical treatment, both short-term and long-term. Furthermore, its prediction window can be extended by several months, providing a basis for earlier treatment of potential risks from postoperative tumor recurrence or metastasis.

[0051] Optionally, in some embodiments of the present invention, the miRNA is derived from the plasma microcellular extracellular vesicles of the subject.

[0052] Small extracellular vesicles, secreted by cells, have a diameter of 40-150 nm and can circulate in bodily fluids such as blood and urine. These vesicles carry signaling molecules derived from the mother cell. Vesicles secreted by tumor cells can transport important signaling molecules within the tumor microenvironment, promoting tumor progression and metastasis. Optionally, in some embodiments of the present invention, the aforementioned detection of minimal residual disease at various stages includes the following stages: before neoadjuvant therapy for breast cancer, after neoadjuvant therapy, before surgery, and short-term and long-term postoperative detection of minimal residual disease.

[0053] Optionally, in some embodiments of the present invention, the system further includes a result display module for displaying the diagnostic conclusions obtained by the diagnostic module.

[0054] Optionally, in some embodiments of the present invention, the result display module displays the diagnostic results by means of screen display, sound broadcast, or printing.

[0055] It should be noted that the sequencing reads of miRNAs and their isoforms can be obtained using sequencing technologies commonly used in the field, such as Illumina's iSeq100, MiniSeq, MiSeq, NextSeq550, NextSeq2000, and NovaSeq6000 sequencers based on PE150 or SE50 sequencing technologies; and BGI's MGI sequencing platform based on SE100, PE100, and PE150 sequencing technologies, etc.

[0056] It should be noted that the methods for extracting and separating plasma microcellular extracellular vesicles and enriching miRNA and its isoforms used in this invention can be performed using conventional techniques and kits in the art.

[0057] On the other hand, the present invention provides a kit for early auxiliary diagnosis of breast cancer and / or detection of minimal residual disease at various stages. The kit contains reagents for detecting mature miRNAs and their isoforms. The detection results of the kit provide sequencing information to the information acquisition module of the system described above.

[0058] Based on the content provided in this invention, any reagent used in the art to detect mature miRNAs and their isoforms, as long as the result provides read values ​​of miRNAs and their isoforms for obtaining the aforementioned system, falls within the scope of protection of this invention. Specific reagents for detecting miRNAs and their isoforms are readily available to those skilled in the art. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A schematic diagram of the method for obtaining mature miRNAs and their isoforms from a subject in Example 1;

[0061] Figure 2 This is the ROC curve for prediction using an early auxiliary diagnostic model for breast cancer in Example 1;

[0062] Figure 3Example 2 shows the statistical chart of positive predictive values ​​for each metastatic lesion of the model of the present invention in patients who were followed up after breast cancer surgery using the detection model of minimal residual disease at each stage of breast cancer.

[0063] Figure 4 This is a statistical chart showing the timeframe from the appearance of positive MRD signals in the minimal residual disease detection model at each stage in Example 2 to the final confirmation of pathological recurrence of breast cancer at different molecular subtypes during clinical follow-up. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0065] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0066] Example 1

[0067] This embodiment provides an early auxiliary diagnostic model for breast cancer and verifies its diagnostic performance.

[0068] 1. The threshold formula for the early auxiliary diagnostic model for breast cancer in this embodiment is as follows:

[0069] S1 = ratio1;

[0070] S1 represents the risk threshold for breast cancer in the subject; ratio1 represents the sum of the ratios of the sequencing reads of the 30 target miRNA mature forms to the sequencing reads of their respective miRNA-1nt isoforms.

[0071] The formula for calculating ratio1 is as follows:

[0072] ratio1=a1+a2+a3+a4+a5+a6+a7+a8+a9+a10+a11+a12+a13+a14+a15+a16+a17+a18+a19+a20+a21+a22+a23+a24+a25+a26+a27+a28+a29+a30;

[0073] Where a1-a30 represent the ratios of the mature read values ​​of 30 miRNAs to the sequencing read values ​​of their respective miRNA-1nt isoforms.

[0074] The 30 mature miRNAs used for different molecular subtypes of breast cancer diagnosis vary, as shown in Table 1.

[0075] Table 1

[0076]

[0077] The diagnostic method using the above thresholds is as follows:

[0078] When diagnosing Luminal A type breast cancer, if the subject's S1 score is ≤16.2, the patient is judged to be positive for breast cancer and the molecular subtype is Luminal A. If S1 is >16.2, the patient is judged to be negative or to have benign breast lesions.

[0079] When diagnosing Luminal B type breast cancer, if the subject's S1 score is ≤15.6, the patient is judged to be positive for breast cancer and the molecular subtype is Luminal B. If S1 is >15.6, the patient is judged to be negative or to have benign breast lesions.

[0080] When diagnosing HER2-positive breast cancer, if the subject's S1 is ≤14.8, the subject is judged to be breast cancer positive and the molecular subtype is HER2 positive; if S1 is >14.8, the subject is judged to be negative or benign breast lesions.

[0081] When diagnosing triple-negative breast cancer, if the subject's S1 score is ≤15.5, the patient is considered to have positive breast cancer and a molecular subtype of triple-negative breast cancer (TNBC). If S1 score is >15.5, the patient is considered to have negative breast cancer or a benign breast lesion.

[0082] 2. Utilize the above model to verify its diagnostic performance:

[0083] The study included 300 participants with Breast Imaging Reporting and Data System (BI-RADS) classifications of 4 and 5 who had not yet undergone tissue biopsy; existing techniques were used and referenced... Figure 1 The procedure obtains the read values ​​of the subject's miRNA and its isoform miRNA-1nt; after sorting by read values, the ratio1 value is obtained; for example, after analyzing a subject's microRNA sequencing data, the reads of the mature miRNA and miRNA-1nt are obtained as shown in Table 2 below:

[0084] Table 2

[0085]

[0086] In the table, the value of a = the number of reads of mature miRNA / the number of reads of miRNA-1nt isoform.

[0087] Referring to Table 1, the a values ​​of the 30 miRNAs required for the calculation of Luminal A were extracted from Table 2 above and summed, resulting in an S1 value of 85.01.

[0088] The S1 value of 30 microRNAs of Luminal B type is 66.63;

[0089] The S1 value of 30 microRNAs in HER2-positive individuals was 11.82;

[0090] The S1 value of the 30 microRNAs of the TNBC type is 36.32.

[0091] Based on the threshold criteria for each subtype in the early auxiliary diagnostic model for breast cancer, the subject was determined to be breast cancer positive and the molecular subtype was HER2 positive.

[0092] The above diagnostic methods were used to diagnose and predict benign or malignant breast cancer and its molecular subtype. These results were then compared with the subjects' final clinicopathological diagnoses to evaluate the value of early adjuvant diagnosis of breast cancer. Related results can be found in [link to relevant results]. Figure 2 Tables 3-7.

[0093] For all molecular subtypes of breast cancer, the auxiliary diagnostic model provided in this embodiment has a sensitivity of 100%, a specificity of 96%, and an AUC as high as 0.982 for early auxiliary diagnosis. (See attached image.) Figure 2 .

[0094] After excluding 12 cases for which no pathological diagnosis information was available, the positive predictive value for the Luminal A molecular subtype of breast cancer was 93.9%, and the negative predictive value was 100%.

[0095] The positive predictive value for the Luminal B molecular subtype of breast cancer is 94.7%, and the negative predictive value is 98.7%.

[0096] The positive predictive value for HER2-positive molecular subtype of breast cancer is 93.3%, and the negative predictive value is 99.4%.

[0097] The positive predictive value for triple-negative (TNBC) molecular subtype of breast cancer is 84.6%, and the negative predictive value is 98.7%.

[0098] Table 3

[0099]

[0100] Table 4

[0101]

[0102] Table 5

[0103]

[0104] Table 6

[0105]

[0106] Table 7

[0107]

[0108] Example 2

[0109] This embodiment provides a model for assessing residual microlesions in breast cancer and validating its diagnostic performance.

[0110] 1. The threshold formula for residual microscopic lesions in breast cancer in this embodiment is as follows:

[0111] S2 = ratio2 + ratio3;

[0112] S2 represents the risk threshold for the subject to develop breast cancer; ratio2 represents the sum of the ratios of the sequencing reads of the 50 target miRNA matures to the sequencing reads of their respective miRNA-1nt isoforms; wherein, the 50 target miRNA matures are composed of the top 50 miRNA matures whose sequencing reads of their miRNA-1nt isoforms are arranged from largest to smallest.

[0113] The formula for calculating ratio2 is as follows:

[0114] ratio2=b1+b2+b3+b4+b5+b6+b7+b8+b9+b10+b11+b12+b13+b14+b15+b16+b17+b18+b19+b20+b21+b22+b23+b24+b25+b 26+b27+b28+b29+b30+b31+b32+b33+b34+b35+b36+b37+b38+b39+b40+b41+b42+b43+b44+b45+b46+b47+b48+b49+b50;

[0115] Here, b1-b50 represent the ratio of the read values ​​of 50 mature miRNAs to the sequencing read values ​​of their respective miRNA-1nt isoforms; the 50 mature miRNAs are composed of the top 50 mature miRNAs whose sequencing read values ​​of their miRNA-1nt isoforms are arranged from largest to smallest.

[0116] ratio3 represents the sum of the ratios of the sequencing reads of 50 target miRNA matures to the sequencing reads of their respective miRNA-2nt isoforms, wherein the 50 target miRNA matures are composed of the top 50 miRNA matures whose sequencing reads of their miRNA-2nt isoforms are arranged from largest to smallest.

[0117] The formula for calculating ratio3 is as follows:

[0118] ratio3=c1+c2+c3+c4+c5+c6+c7+c8+c9+c10+c11+c12+c13+c14+c15+c16+c17+c18+c19+c20+c21+c22+c23+c24+c25+c 26+c27+c28+c29+c30+c31+c32+c33+c34+c35+c36+c37+c38+c39+c40+c41+c42+c43+c44+c45+c46+c47+c48+c49+c50;

[0119] Wherein, c1-c50 represent the ratio of the read values ​​of 50 mature miRNAs to the sequencing read values ​​of their respective miRNA-2nt isoforms; the 50 mature miRNAs are composed of the top 50 mature miRNAs whose sequencing read values ​​of their miRNA-2nt isoforms are arranged from largest to smallest.

[0120] The diagnostic method using the above thresholds is as follows:

[0121] If S2 < 20.6, it is determined that there are residual breast cancer lesions in the patient, and the MRD result is positive.

[0122] If S2 > 48.9, it is determined that there are no residual breast cancer lesions in the subject's body, and the MRD result is negative;

[0123] If 20.6≤S2≤48.9, it is recommended that the subject be retested within 3 months or undergo enhanced clinical follow-up.

[0124] 2. Utilize the above model to verify its diagnostic performance:

[0125] 150 breast cancer patients who were pathologically diagnosed after surgery were enrolled, and plasma small cell extracellular vesicle miRNA sequencing was performed within one month after surgery (same as in Example 1), and the S2 value was obtained.

[0126] Table 8 below shows all the miRNAs and their different isoform reads detected in a certain patient.

[0127] Table 8

[0128]

[0129] In the table, b value = mature miRNA reads value / miRNA-1nt isoform reads value, c value = mature miRNA reads value / miRNA-2nt isoform reads value;

[0130] Sort the top 50 miRNA-1nt isoform reads from the table above (from largest to smallest), extract the b-values ​​of these 50 mature / miRNA-1n, and sum them. The resulting value, ratio2, is 6.09.

[0131] Then, sort the top 50 miRNA-2nt isoform reads (from largest to smallest), extract the c-values ​​of these 50 mature / miRNA-2nt isoforms, and sum them up. The resulting value, ratio3, is 13.62.

[0132] Therefore, the sum of ratio2 and ratio3 is 19.71, meaning the patient's S2 = 19.71. According to the assessment method of the residual breast cancer microlesion assessment model, an S2 less than 20.60 indicates that the subject has residual breast cancer lesions, the MRD result is positive, and breast cancer has recurred at the molecular level. The S2 calculation method for other patients is the same.

[0133] If S2 < 20.60, the patient was excluded from follow-up, indicating that the current neoadjuvant therapy or surgery had not effectively controlled the breast cancer lesions. Subsequently, these patients underwent repeated testing every 3-6 months to obtain S2 data for all patients until any patient's S2 value first showed < 20.60. At this point, testing was discontinued for that patient, indicating that at the molecular level, the patient had experienced a transition from complete clinical remission to molecular recurrence. The patient was then followed up routinely until radiological or pathological recurrence or metastasis was detected.

[0134] Based on follow-up statistics, this embodiment measures the detection rate of metastatic lesions in different organs and the window period from MRD positive to clinical follow-up confirmation of recurrence and metastasis in patients with metastatic lesions in different organs.

[0135] One month post-surgery, 7 out of 150 breast cancer patients still had an S2 value less than 20.60. After excluding these 7 patients, a follow-up of 143 patients revealed that 61 eventually developed bone metastases, 22 developed lung metastases, 19 developed liver metastases, 14 developed brain metastases, and 27 developed multiple organ metastases (including simultaneous lung and brain metastases, or simultaneous bone, lung, and liver metastases, etc.).

[0136] Using S² < 20.60 as the criterion for determining the presence of MRD, the detection rate of bone metastasis in this embodiment is 93.44%, and the detection rate of lung metastasis is 90.91%.

[0137] The detection rate of liver metastasis was 94.74%, the detection rate of brain metastasis was 85.71%, and the detection rate of multiple organ metastasis was 100%.

[0138] The MRD detection rate in patients with brain metastases from breast cancer exceeded 85%, significantly higher than the 17% achieved by the current ctDNA testing protocol.

[0139] These 134 breast cancer patients (S2 value < 20.60, and 9 other patients with S2 value ≤ 48.90 were not included in the statistics) were grouped according to the molecular subtype of pathological diagnosis. The interval between the detection of MRD positivity before clinical recurrence and metastasis of Luminal A, Luminal B, HER2 positive and TNBC types was statistically analyzed.

[0140] The early onset time for Luminal A type 2 encephalopathy ranged from 17 to 42 months, with a median of 31.5 months.

[0141] The early intervention time for Luminal B is between 22 and 40 months, with a median of 32.5 months.

[0142] The early detection time for HER2 positivity ranges from 18 to 31 months, with a median of 20.5 months.

[0143] The lead time for TNBC is between 12 and 22 months, with a median of 15.5 months.

[0144] Compared to the currently common technical approach of determining MRD positivity based on ctDNA sequencing, the model in this embodiment detects MRD positivity approximately 7 months earlier for TNBC, approximately 5.5 months earlier for HER2 positivity, and approximately 6-6.5 months earlier for Luminal A and B.

[0145] This embodiment demonstrates that the model significantly prolongs the observation and clinical intervention period for long-term postoperative monitoring of breast cancer patients, which will significantly benefit the patient's prognosis.

[0146] In summary, the present invention has the following advantages: 1. In the field of early adjuvant diagnosis of breast cancer, multiple miRNA isoforms in plasma small extracellular vesicles are detected using high-throughput sequencing technology with 1 ml of whole blood, yielding raw sequencing data as low as 0.5 Gb. Data analysis is completed within 3 days, enabling accurate differentiation between benign breast diseases and various molecular subtypes of breast cancer, with a diagnostic AUC exceeding 0.95. 2. By introducing the expression profile of dozens of plasma small extracellular vesicle miRNAs for breast cancer, this invention solves the problem of insufficient specificity caused by the use of similar miRNAs in the diagnosis of various common and frequently occurring tumors. 3. By adjusting the detection of target points of plasma small extracellular vesicle miRNA isoforms within the model and the ratio of mature to isoform, the invention enables the detection of minimal residual disease before and after neoadjuvant therapy, before surgery, and in the short and long term after surgery for different molecular subtypes of breast cancer.

[0147] This invention provides a reliable predictive means for the early auxiliary diagnosis of breast cancer and the monitoring of minimal residual disease after surgery; it also provides a more accurate diagnostic basis for early treatment of breast cancer patients.

[0148] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A system for early auxiliary diagnosis of breast cancer and / or detection of minimal residual disease at various stages, characterized in that, The system includes an information acquisition module, a calculation module, and a diagnostic module; The information acquisition module is used to perform the step of acquiring sequencing information of multiple mature miRNAs and their isoforms of the subject; the sequencing information includes sequencing reads values; The calculation module is used to perform the step of substituting the sequencing reads value into the calculation model to calculate the threshold; the threshold is determined by the ratio of the reads value of the mature miRNA to its corresponding isoform. The diagnostic module is used to perform the operation of judging the health status of the subject based on a threshold.

2. The system according to claim 1, characterized in that, The isoforms include: miRNA-1nt isoform and miRNA-2nt isoform.

3. The system according to claim 2, characterized in that, The computational model includes an early auxiliary diagnostic model for breast cancer. The mathematical formula for the early auxiliary diagnostic model for breast cancer includes: S1=ratio1; Wherein, S1 represents the risk threshold for the subject to develop breast cancer; ratio1 represents the sum of the ratios of the sequencing reads values ​​of multiple mature miRNAs to the sequencing reads values ​​of their respective miRNA-1nt isoforms; When used for the diagnosis of Luminal A breast cancer, the multiple mature miRNAs consist of the following 30 miRNAs: hsa-miR-100-5p, hsa-miR-106-3p, hsa-miR-10a-5p, hsa-miR-122-5p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-146b-5p, hsa-miR-148a-3p, hsa-miR-15a-5p, hsa-miR-182-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199 a-3p, hsa-miR-199b-3p, hsa-miR-223-3p, hsa-miR-26a-3p, hsa-miR-26b-5p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-320a-3p, hsa-miR-32 8-3p, hsa-miR-409-3p, hsa-miR-483-5p, hsa-miR-486-5p, hsa-miR-584-5p, hsa-miR-7-5p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p; When used for the diagnosis of Luminal B breast cancer, the multiple mature miRNAs consist of the following 30 miRNAs: hsa-let-7f-5p, hsa-miR-100-5p, hsa-miR-10a-5p, hsa-miR-126-3p, hsa-miR-139-3p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-146b-5p, hsa-miR-15a-5p, hsa-miR-183-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199a -5p, hsa-miR-199b-3p, hsa-miR-223-3p, hsa-miR-25-3p, hsa-miR-26a-3p, hsa-miR-29a-3p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-30e-5 p, hsa-miR-320a-3p, hsa-miR-409-3p, hsa-miR-483-3p, hsa-miR-483-5p, hsa-miR-532-3p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p; When diagnosing HER2-positive breast cancer, the multiple mature miRNAs consist of the following 30 miRNAs: hsa-let-7a-5p, hsa-let-7d-3p, hsa-miR-100-5p, hsa-miR-10a-5p, hsa-miR-127-3p, hsa-miR-129-5p, hsa-miR-1307-3p, hsa-miR- 142-3p, hsa-miR-143-3p, hsa-miR-145-5p, hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-185-5p, h sa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199b-3p, hsa-miR-223-3p, hsa-miR-26a-3p, hsa-miR-26a-5p, hsa-miR-30b-5p, hsa-miR -30c-5p, hsa-miR-320a-3p, hsa-miR-375-3p, hsa-miR-409-3p, hsa-miR-483-5p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p; When diagnosing triple-negative breast cancer, the multiple mature miRNAs consist of the following 30 miRNAs: hsa-miR-100-5p, hsa-miR-10a-5p, hsa-miR-1287-5p, hsa-miR-142-3p, hsa-miR-143-3p, hsa-miR-146b-5p, hsa-miR-155-5p, hsa-miR-15a-5p, hsa-miR-185-5p, hsa-miR-186-5p, hsa-miR-193a-5p, hsa-miR-199b-3p, hsa-miR-19a-3p, hsa-miR -210-5p, hsa-miR-211-5p, hsa-miR-218-5p, hsa-miR-223-3p, hsa-miR-26a-3p, hsa-miR-30b-5p, hsa-miR-30c-5p, hsa-miR-3130-3p, hsa-miR-320 a-3p, hsa-miR-338-3p, hsa-miR-34a-5p, hsa-miR-409-3p, hsa-miR-4485-3p, hsa-miR-483-5p, hsa-miR-96-5p, hsa-miR-98-5p, and hsa-miR-99b-5p.

4. The system according to claim 3, characterized in that, The diagnostic module makes a judgment based on the risk threshold S1 of the early auxiliary diagnostic model for breast cancer, and the judgment method is as follows: When diagnosing Luminal A type breast cancer, if the subject's S1 score is ≤16.2, the patient is judged to be positive for breast cancer and the molecular subtype is Luminal A. If S1 is >16.2, the patient is judged to be negative or to have benign breast lesions. When diagnosing Luminal B type breast cancer, if the subject's S1 score is ≤15.6, the patient is judged to be positive for breast cancer and the molecular subtype is Luminal B. If S1 is >15.6, the patient is judged to be negative or to have benign breast lesions. When diagnosing HER2-positive breast cancer, if the subject's S1 is ≤14.8, the subject is judged to be breast cancer positive and the molecular subtype is HER2 positive; if S1 is >14.8, the subject is judged to be negative or benign breast lesions. When diagnosing triple-negative breast cancer, if the subject's S1 score is ≤15.5, the subject is judged to be positive for breast cancer and the molecular subtype is triple-negative breast cancer. If S1 >15.5, the subject is judged to be negative or a benign breast lesion.

5. The system according to claim 2, characterized in that, The computational model includes a model for assessing residual microlesions; The mathematical formula for the breast cancer minimal residual disease assessment model includes: S2 = ratio2 + ratio3; S2 represents the risk threshold for the subject having minimal residual disease in breast cancer; ratio2 represents the sum of the ratios of the sequencing reads of 50 target miRNA matures to the sequencing reads of their respective miRNA-1nt isoforms, wherein the 50 target miRNA matures are composed of the top 50 miRNA matures whose sequencing reads of their miRNA-1nt isoforms are arranged from largest to smallest. ratio3 represents the sum of the ratios of the sequencing reads of 50 target miRNA matures to the sequencing reads of their respective miRNA-2nt isoforms, wherein the 50 target miRNA matures are composed of the top 50 miRNA matures whose sequencing reads of their miRNA-2nt isoforms are arranged from largest to smallest.

6. The system according to claim 5, characterized in that, The diagnostic module makes a judgment based on the risk threshold S2 of the residual microlesion assessment model, and the judgment method is as follows: If S2 < 20.6, then the patient is diagnosed with minimal residual disease of breast cancer, and the MRD result is positive. If S2 > 48.9, it is determined that there are no microresidual lesions of breast cancer in the subject, and the MRD result is negative; If 20.6≤S2≤48.9, it is recommended that the subject be retested within 3 months or undergo enhanced clinical follow-up.

7. The system according to any one of claims 1-6, characterized in that, The miRNA was derived from the plasma microcellular extracellular vesicles of the subject.

8. The system according to any one of claims 1-6, characterized in that, The system also includes a result display module, which is used to display the diagnostic conclusions obtained by the diagnostic module.

9. The system according to claim 8, characterized in that, The result display module displays the diagnostic results through screen display, sound broadcast, or printing.

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