A method and device for monitoring circulating minimal residual disease in diffuse large b-cell lymphoma

Abstract

The application discloses a diffuse large B-cell lymphoma circulating minimal residual monitoring method and device, and belongs to the technical field of cell detection. The method comprises the following steps: acquiring original sequencing data of tumor tissue of a target patient; identifying a malignant B cell population in the tumor tissue according to the original sequencing data, screening specific expression genes based on the malignant B cell population, and constructing a personalized gene package; acquiring peripheral blood sequencing data of the target patient; identifying the gene sequence of the B cell population according to the peripheral blood sequencing data and B cell characteristic genes; and detecting the expression of the personalized gene package in the gene sequence of the B cell population to determine the presence state of the minimal residual lesion of the diffuse large B-cell lymphoma. The above scheme improves the detection accuracy of the minimal residual lesion of the diffuse large B-cell lymphoma circulating in the patient's body.

Description

Technical Field

[0001] This invention relates to the field of cell detection technology, and in particular to a method and device for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma. Background Technology

[0002] Currently, the main means of assessing treatment response and determining recurrence in diffuse large B-cell lymphoma is imaging, especially PET-CT.

[0003] In diffuse large B-cell lymphoma, a high baseline level of total metabolic tumor volume (TMTV) or total lesion glycolysis is associated with a poorer prognosis. Interim PET-CT (iPET) assessment is performed on patients after two treatment cycles; a negative result can be used as a basis for treatment downgrade. However, there is no consensus on the selection of DS5 and ΔSUVmax assessment methods, and further research is needed to guide treatment. Therefore, changing the treatment plan based on iPET assessment results is not currently recommended. The assessment of patient relapse status mainly relies on clinical symptoms, PET-CT scans, and repeat biopsies. However, PET-CT or CT has not shown effectiveness in the systematic follow-up of patients who have responded to first-line treatment. Repeated PET-CT irradiation increases the risk of radiation exposure, and imaging examinations lack sensitivity for the early identification of patients who have failed treatment and recurrent lesions. Furthermore, repeat biopsies of tumors after multiple treatments present certain difficulties.

[0004] Therefore, there is an urgent need for a method to monitor circulating minimal residual disease in diffuse large B-cell lymphoma. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for monitoring circulating minimal residual disease (RSD) in diffuse large B-cell lymphoma to address the above problems, thereby improving the detection accuracy of RSD in patients.

[0006] This application provides a method for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma, the method comprising: Obtain raw sequencing data of tumor tissue from the target patient; Based on the original sequencing data, the malignant B cell population in the tumor tissue was identified, and specific expression genes were screened based on the malignant B cell population to construct a personalized gene package. Peripheral blood sequencing data of the target patient was obtained; the peripheral blood sequencing data was obtained by sequencing the peripheral blood of the patient after treatment; Based on the peripheral blood sequencing data and B cell characteristic genes, the gene sequences of the B cell population were identified; The expression of personalized gene packs was detected in the gene sequence of the B cell population to determine the presence of minimal residual disease in the diffuse large B-cell lymphoma.

[0007] This application also provides a device for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma, the device comprising: Sequencing data acquisition module, used to acquire raw sequencing data of tumor tissue from the target patient; A gene package construction module is used to identify the malignant B cell population in the tumor tissue based on the original sequencing data, and to screen specifically expressed genes based on the malignant B cell population to construct a personalized gene package. The sequencing data acquisition module is used to acquire peripheral blood sequencing data of the target patient; the peripheral blood sequencing data is obtained by sequencing the peripheral blood of the patient after treatment; A cell population identification module is used to identify the gene sequence of a B cell population based on the peripheral blood sequencing data and B cell characteristic genes. The lesion status determination module is used to detect the expression of personalized gene packages in the gene sequence of the B cell population to determine the presence status of minimal residual lesions in the diffuse large B-cell lymphoma.

[0008] In another aspect, this application also provides an electronic device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described method for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma.

[0009] This application also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the above-described method for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma.

[0010] Compared with the prior art, the technical solution provided in this application has the following advantages: For target patients with diffuse large B-cell lymphoma, raw sequencing data of the tumor tissue can be obtained first. Then, based on this raw sequencing data, the malignant B-cell population within the tumor tissue can be identified, and specific expressed genes can be screened to construct a personalized gene package for the target patient. For post-treatment patients, peripheral blood can be collected and sequenced. Based on this peripheral blood sequencing data and B-cell characteristic genes, the gene sequences of the B-cell population within the peripheral blood sequencing data can be identified. After obtaining the B-cell gene sequences, the expression of the personalized gene package can be detected to determine the status of minimal residual disease (MRD) in diffuse large B-cell lymphoma. This approach allows for the construction of personalized gene packages for different patients, and the expression results of these personalized gene packages in post-treatment peripheral blood sequencing data can be used to determine the status of MRD in diffuse large B-cell lymphoma, thereby improving the accuracy of detecting circulating minimal residual disease (MRD) in patients with diffuse large B-cell lymphoma. Attached Figure Description

[0011] Figure 1 This is a schematic diagram showing the clinical treatment information and sampling time of three subjects obtained in the embodiments of this application.

[0012] Figure 2 This is the spatial transcriptome sequencing atlas of DLBCL tumor biopsy tissues from subjects Sample 1 and Sample 2 in the example.

[0013] Figure 3 This is the spatial transcriptome sequencing map of the DLBCL tumor biopsy tissue of subject Sample3 in the example.

[0014] Figure 4 This is a schematic diagram illustrating the identification of malignant B cell populations in subject scRNA-seq data.

[0015] Figure 5 This involves the identification of B cell subsets with light chain-restricted expression in the spatial transcriptome sequencing data of the subjects in the example.

[0016] Figure 6 This example demonstrates how the spatial transcriptome of subject Sample 2, combined with scRNA-seq and BCR-seq, was used to identify circulating tumor cells.

[0017] Figure 7 This is a schematic diagram illustrating how subjects used scRNA-seq and BCR-seq data to identify malignant B cells in an example.

[0018] Figure 8This example demonstrates how subject Sample 3 spatial transcriptome combined with scRNA-seq and BCR-seq can be used to identify circulating tumor cells. Figure 9 This is a schematic diagram of the process of the circulating minimal residual lesion technology involved in this application.

[0019] Figure 10 This is a reference schematic diagram of a cyclic micro-residue monitoring technology involved in an embodiment of this application.

[0020] Figure 11 This is a schematic diagram of the structure of a circulating minimal residual disease monitoring device for diffuse large B-cell lymphoma provided in an embodiment of this application.

[0021] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an optional embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Non-Hodgkin's lymphoma (NHL) is a heterogeneous group of tumors originating from lymph nodes or lymphatic tissue, with different subtypes exhibiting varying treatment regimens and prognoses. NHL treatment typically employs different strategies based on the patient's disease subtype, tumor stage, and prognostic stratification. Diffuse large B-cell lymphoma (DLBCL) is the most common type of NHL, accounting for approximately 30%–40%. DLBCL exhibits high heterogeneity in cellular composition and biological behavior, characterized by rapid progression, high invasiveness, and the ability to affect any organ in the body. Most patients with localized disease and approximately 50% of patients with advanced disease can achieve a cure after first-line treatment, with a 5-year relative survival rate of 64%. However, 30%–40% of patients relapse within 2 years of diagnosis. These patients who relapse after remission or fail first-line treatment have poor treatment outcomes, with a median overall survival of only 6 months. Early identification of patients with disease relapse and treatment failure can maximize patient survival and quality of life.

[0024] Currently, imaging techniques, particularly PET-CT, are the primary means of assessing treatment response and determining relapse in diffuse large B-cell lymphoma. Studies have shown that high baseline levels of total metabolic tumor volume (TMTV) or total lesion glycolysis are associated with poorer prognosis. Interim PET (iPET) assessments are performed on patients after two treatment cycles; negative results can be used as a basis for downgrading treatment. However, there is no consensus on the selection of DS5 and ΔSUVmax assessment methods, and further research is needed to guide treatment. Therefore, the current NCCN guidelines do not recommend changing treatment plans based on iPET assessment results. Assessment of relapsed / refractory status is mainly based on clinical symptoms, PET-CT scans, and repeat biopsies. However, PET-CT or CT has not shown effectiveness in systematic follow-up of patients who have responded to first-line treatment. Repeated PET-CT irradiation increases radiation exposure risk, and imaging examinations lack sensitivity for early identification of patients who have failed treatment and recurrent lesions. Furthermore, repeat biopsies of tumors after multiple treatments present certain difficulties. These factors pose significant challenges to the selection of the optimal timing for intensive treatment and restarting anti-tumor therapy in DLBCL. In-depth exploration of precise monitoring technologies for the disease status of DLBCL patients is beneficial for risk stratification, guiding targeted and immunotherapies, detecting early relapses, improving patient survival, and refining the treatment modality for DLBCL. Minimal residual disease (MRD) refers to residual tumor cells that remain in the patient's body after radical treatment but are undetectable by imaging methods. Multiple studies and clinical practices have demonstrated the value of MRD monitoring in guiding post-remission treatment and identifying early relapses.

[0025] To achieve monitoring of circulating minimal residual disease in diffuse large B-cell lymphoma, and to obtain the technical solution presented in this application, the following research was conducted: 1. Acquisition and processing of raw sequencing data from tumor tissue of the target patient like Figure 1 As shown, in this embodiment, tumor biopsy specimens were obtained from three subjects with relapsed or refractory DLBCL. Two of the samples came from subjects who were clinically assessed as having achieved remission (Sample 1, Sample 2), and two samples came from the same subject who did not undergo clinical assessment after treatment (Sample 3_1, Sample 3_2).

[0026] Spatial transcriptome sequencing was performed on paraffin sections of the subjects' puncture tissue. Active tumor regions were identified by the gene UMI values ​​at statistical spots, and the data were processed using the standardization function (SCTransform) to eliminate batch effects.

[0027] The integrated data was then dimensionality-reduced using UMAP, and the "FindAllMarkes" function was used to identify differentially expressed genes in cell clusters (P.value < 0.1 and |Log2foldchange| > 1), combined with cell markers for cell annotation. In Sample 1 and Sample 2 of this embodiment, seven cell types were identified, including necrotic cells, muscle cells, neutrophils, cells with pericyte and fibroblast characteristics, cells with pericyte, fibroblast, and T cell characteristics, cells with endothelial and fibroblast characteristics, and DLBCL. Based on the genes significantly expressed in each cluster, DLBCL was divided into four categories: DLBCL_C1_SOX4, DLBCL_C2_RGS1, DLBCL_C3_CCL17, and DLBCL_C4_ACTA1. Figure 2 In Example 3, nine cell types were identified, including fibroblasts, endothelial cells, epithelial cells, cells with characteristics of smooth muscle cells, pericytes, and fibroblasts, and unknown cells 1-5. Figure 3 ).

[0028] 2. Identification of malignant B-cell populations This application achieves the distinction between malignant B cells and normal B cells through the following technical means: This protocol was previously validated using light chain restriction expression analysis in peripheral blood scRNA-seq of healthy subjects (N1-3) and DLBCL subjects who underwent treatment after relapse (T3, 5, 6, 8), demonstrating that this method can identify malignant B cell populations. This protocol defines B cells with light chain restriction expression as those whose cells express κ or λ chains comprising more than 80% of the total population. Figure 4 As shown in Figure C, previous studies have identified B cell subsets (clusters 5 and 6) in the peripheral blood of subjects exhibiting light chain-restricted expression. Cells in cluster 5 predominantly express the λ chain, while cells in cluster 6 predominantly express the κ chain. Further analysis was conducted on the expression of genes associated with DLBCL and mantle cell lymphoma. Figure 4 As shown in Figure D, the cluster 6 population highly expresses the gene BCL6, which is associated with the germinal center, while the B cell population does not express the gene CCND1, which is associated with mantle cell lymphoma.

[0029] Subsequent studies further analyzed the differential gene expression in B cells with light chain restriction and performed GO and KEGG enrichment analyses, such as... Figure 4 Further clustering of B cell subsets exhibiting light chain-restricted expression (E and F) revealed that these cells were primarily found in the T3 and T5 cells of DLBCL patients. GO and KEGG enrichment analyses were performed on these cells based on their sample origin. Figure 4G shows the enrichment of the target cell population in biological functions related to cell proliferation, cell survival, chromatin remodeling, and gene expression, as well as in tumor-specific pathways such as cell proliferation, immune escape, and transcriptional dysregulation.

[0030] In summary, this approach can identify malignant B-cell populations by assessing B-cell light chain restriction expression, DLBCL tumor-related gene expression, and differential gene enrichment analysis of B cells.

[0031] 3. Selection of genes specifically expressed by malignant B cells This protocol identifies differentially expressed genes in tumor cells as malignant B-cell-specific genes. This example analyzes light chain expression in the DLBCL subset of tumor tissue from clinically remissioned subjects. Figure 5 All four DLBCL subsets shown exhibited restricted light chain expression, with Sample 1 showing restricted expression of the λ light chain and Sample 2 showing restricted expression of the κ light chain, consistent with malignant B-cell characteristics. Therefore, genes with high specific expression in these four DLBCL populations were selected as specific tumor marker genes for Samples 1 and 2 (CD78B, IGHM, SOX4, CCR7, RGS1, CCL17, CD52, TCL1A, ACTG1).

[0032] In this example, subject Sample 3 did not identify typical DLBCL cells based on classic cell markers. Further analysis of differentially expressed genes revealed that they were mainly related to B cells and DLBCL tumor characteristic genes (IGKC, FCRLA, TCL1A, IGHG1, LAG3, STAT1, HDAC9, CD48, CD1C, JCHAIN). Light chain restriction expression analysis of all cell subpopulations showed that all subpopulations restricted the expression of κ light chains, as shown in the figure. Combined with the pathology of subject Sample 3 (significant stromal sclerosis), this study concludes that all cell populations identified in the spatial transcriptome sequencing of subject Sample 3 contained tumor cells, including the Unknown_C1-5 cell population. Regions identified as stromal cells such as fibroblasts had a higher proportion of stromal cells, thus masking the characteristics of tumor cells. To avoid the influence of stromal cells, this example obtained differentially expressed genes for each subpopulation in the Unknown_C1-5 subpopulation and all cell subpopulations.

[0033] 3. Identification of circulating minimal residual disease This protocol simultaneously acquires peripheral blood from treated subjects for scRNA-seq and BCR-seq. The obtained data are processed, dimensionality-reduced, clustered, and annotated. In this embodiment, no B cells were identified in the scRNA-seq data of the treated subjects' peripheral blood. BCR-seq sequencing identified 7 and 11 cells containing BCR light and heavy chain information in Samples 2 and 3, respectively. The light chains of these cells were all κ-type, consistent with their corresponding spatial omics results. Therefore, this embodiment considers these cells to be tumor cells, naming them Tumor_suspect_1 and 2. The expression of tumor-specific genes was examined in these Tumor_suspect cells. In this embodiment, no tumor-specific gene expression was observed in any cell subpopulation in the peripheral blood scRNA-seq of subject Sample 2 or in Tumor_suspect_1 cells. Further GO and KEGG enrichment analysis showed that the enriched pathways in this cell group were related to B cell function and B cell receptor pathways, but no upregulation of tumor-related functions was observed. Figure 6 ).

[0034] The expression of tumor-specific genes was examined in various cell subsets of peripheral blood scRNA-seq from subjects Sample 3 in the example. Figure 7 ),like Figure 8 The characteristic genes of the Unknown_C3 subset in the spatial transcriptome are specifically expressed in the Tumor_suspect_2 population. Further processing of the spatial transcriptome sequencing data was performed to remove characteristic genes from stromal cells (Fib, Endothelia, Epithelial, SMCPerFib). Differential gene analysis was conducted on all cell subpopulations, and expression patterns were examined in scRNA-seq data. It was observed that the Tumor_suspect population still exhibits the characteristic expression patterns of the Unknown_C3 subset. Therefore, in this embodiment, the Tumor_suspect population is considered to originate from circulating tumor cells of the Unknown_C3 subset from the primary tumor lesion.

[0035] Based on the above research results, it is evident that under the combined detection of ultra-high resolution multi-omics technology, residual tumor cells with primary tumor characteristics can be accurately captured in circulation, and these captured tumor cells can serve as evidence of circulating minimal residual disease (MRD). Therefore, this application provides a monitoring method that can effectively correlate with circulating MRD. Its principle differs from ctDNA detection methods, but it offers higher individualized accuracy and broader application scenarios, effectively supplementing and upgrading existing MRD monitoring technologies. Figure 9 This is a schematic diagram of the process for the circulating minimal residual lesion technology involved in this application. Figure 9 As shown, the method flow includes: Step 901: Obtain raw sequencing data of the tumor tissue from the target patient.

[0036] Optionally, in this embodiment of the application, the raw sequencing data of the tumor tissue includes at least one of spatial transcriptome sequencing data, single-cell RNA sequencing data, or single-nuclear RNA sequencing data.

[0037] In this embodiment, tumor tissue samples are first obtained from the target patient before treatment. These samples may originate from lymph node biopsy tissue or lesion puncture tissue. At least one of single-cell transcriptome sequencing (scRNA-seq), single-nuclear RNA sequencing (snRNA-seq), or spatial transcriptome sequencing is performed on the tissue to obtain raw expression matrix data at single-cell resolution.

[0038] Step 902: Identify the malignant B cell population in the tumor tissue based on the original sequencing data, and screen for specifically expressed genes based on the malignant B cell population to construct a personalized gene package.

[0039] Specifically, in the embodiments of this application, differential gene expression and light chain expression analysis can be performed on the raw sequencing data of tumor tissue to identify malignant B cell populations and screen for genes specifically expressed in malignant B cell populations in order to construct personalized gene packages.

[0040] Among them, specifically expressed genes are those that are highly expressed in malignant B cell populations and lowly expressed in non-malignant B cells and other cell types.

[0041] Specifically, the specifically expressed genes include at least one of the following: DLBCL marker genes, oncogenes with abnormally high expression, and transcripts associated with B-cell receptor rearrangements.

[0042] Specifically, in the embodiments of this application, cell annotation can be performed in tumor tissue based on differential gene expression and classical cell marker genes to identify B cell populations and further distinguish malignant B cell populations. Malignant B cells can be identified based on one or a combination of the following characteristics: light chain restricted expression (abnormal κ / λ ratio), BCR clonal amplification characteristics, high expression of proliferation-related genes, and abnormal expression patterns of genes related to germinal centers.

[0043] This application can use the FindAllMarkers function to statistically analyze the differentially expressed genes in malignant B cells of target patients, and select genes with high expression abundance and certain specificity as specific expression genes based on the statistical results.

[0044] Specifically, in one embodiment of this application, the process of screening for specifically expressed genes from a malignant B-cell population based on differential expression analysis results includes: Differential expression analysis was performed on malignant B cell populations and control cell populations to obtain a candidate gene set; Based on the expression abundance of the candidate genes in the malignant B cell population and their expression specificity relative to the control cell population, the candidate genes are screened to obtain a set of specifically expressed genes.

[0045] The screening process shall satisfy at least one or a combination of the following conditions: The average expression level of the candidate gene in the malignant B cell population was higher than the preset expression threshold. The expression differences of the candidate gene between the malignant B cell population and the control population met the pre-set statistical significance criteria; The expression rate of the candidate gene in the malignant B cell population was higher than that in the control cell population; The candidate gene was enriched in malignant B cell populations, but its expression was restricted in non-malignant B cells or other immune cells. Through the above screening, a gene set with both high expression levels and strong specificity was obtained, which can be used to construct personalized gene packages.

[0046] This application does not limit the number, abundance, or specificity of differentially expressed genes selected, and those skilled in the art can make adaptive adjustments based on actual application scenarios and patient conditions.

[0047] Specifically, the personalized gene package comprises at least one of the following: classic DLBCL-related marker genes (germinal center-related genes, activated B-cell-like genes, proliferation-related genes, etc.), abnormally highly expressed oncogenes, and transcripts related to B-cell receptor rearrangement. For example, in embodiments of this application, differentially expressed genes of DLBCL cell populations identified in spatial transcriptome sequencing can be used as tumor marker genes, including differentially expressed genes of various DLBCL subpopulations in Samples 1 and 2 (CD78B, IGHM, SOX4, CCR7, RGS1, CCL17, CD52, TCL1A, ACTG1), and differentially expressed genes of the Unknown_C1-5 subpopulation and all cell subpopulations in Sample 3 after removing mesenchymal cell-related genes.

[0048] Step 903: Obtain peripheral blood sequencing data from the target patient.

[0049] The peripheral blood sequencing data was obtained by sequencing the peripheral blood of the patient after treatment.

[0050] Peripheral blood samples were collected after the patient completed treatment (such as chemotherapy or immunotherapy). PBMCs were isolated by Ficoll density gradient centrifugation, followed by single-cell RNA sequencing and BCR sequencing or other high-throughput transcriptome sequencing.

[0051] Step 904: Identify the gene sequences of the B cell population based on the peripheral blood sequencing data and B cell characteristic genes.

[0052] In this embodiment, after obtaining peripheral blood sequencing data from the target patient, the B cell population is first identified based on B cell characteristic genes. For example, B cell characteristic genes may include CD19, CD79A, etc. If the classic B cell population is difficult to identify in the cells, the presence of a B cell population can be determined by whether the cells contain BCR information.

[0053] Specifically, in this embodiment, gene sequences of B cell populations exhibiting clonal amplification characteristics and light chain restriction can be screened from peripheral blood sequencing data based on B cell characteristic genes. In other words, after identifying the B cell population, its clonal characteristics can be further analyzed by examining BCR rearrangement consistency, calculating the clonal amplification ratio, and determining light chain restriction expression. If the expression ratio of one light chain is significantly higher than that of another, or if significant clonal amplification is present, it suggests the possible existence of a (monoclonal) B cell population, which requires special attention.

[0054] Step 905: Detect the expression of personalized gene packs in the gene sequence of the B cell population to determine the presence status of minimal residual disease in the diffuse large B-cell lymphoma.

[0055] Specifically, in the embodiments of this application, the presence of circulating tumor cells in the B cell population can be determined based on the expression level of differentially expressed genes in the gene sequence of the B cell population.

[0056] Based on the above research Figure 8 According to the corresponding content, in the embodiments of this application, if a significant proportion of genes in the personalized gene package are highly expressed in the B cell population, and since the B cell population is a monoclonal B cell population with light chain restriction or clonal amplification characteristics, it can be determined that a circulating tumor cell population exists.

[0057] Furthermore, in this embodiment of the application, in addition to steps 901-905 described above, the proportion of abnormal B cell population can also be estimated through the following steps: B cell populations were sorted from the peripheral blood sample based on B cell-specific markers. Flow cytometry was used to detect the protein expression of at least two characteristic genes of the personalized gene package in the B cell population to determine the proportion of abnormal B cells.

[0058] In other words, when the presence of circulating tumor cell population residue is determined through steps 901-905 above, if the panel contains multiple genes suitable for flow cytometry detection, CD19 can also be sorted. + B cells were analyzed, and the expression of corresponding proteins in the personalized gene package was detected by flow cytometry to further verify and quantify the proportion of abnormal B cell populations.

[0059] When the proportion of abnormal B cells exceeds a preset threshold, it can further support the judgment of the presence of circulating tumor cells. On the other hand, the residual amount of circulating tumor cells in the target patient can be estimated by quantifying the proportion of abnormal B cell population, which makes it easier for doctors to classify the recurrence status of the target patient.

[0060] Figure 10 This is a reference schematic diagram of a cyclic micro-residue monitoring technology involved in an embodiment of this application. For example... Figure 10 As shown, for patients with recurrent DLBCL, biopsy tissue samples from the primary lesion or after recurrence are first obtained for spatial transcriptome sequencing (scRNA-seq or snRNA-seq can also be performed simultaneously, depending on tissue activity and quantity). Cellular characterization analysis of the sequencing data identifies the main malignant B cell population in the patient's tumor tissue, and selects characteristic genes with high specificity and high expression abundance (such as specific surface markers, abnormally highly expressed oncogenes, or rearrangement-related transcripts) to construct a personalized detection gene panel. Subsequently, peripheral blood is collected after the patient's first treatment for BCR sequencing and scRNA-seq. Based on BCR information and B cell characteristic genes, B cell populations are identified. Light chain restriction expression analysis indicates the presence of monoclonal B cells. The expression of the patient's personalized panel genes is then detected in these B cells (if a gene specifically highly expressed in the tumor tissue is also expressed in a certain B cell subset in peripheral blood, it strongly suggests that this cell group is circulating tumor cells), thereby identifying the circulating tumor cell population. If the panel contains multiple genes suitable for flow cytometry detection, CD19 can also be sorted. + B cells were collected and analyzed by flow cytometry to further validate and quantify circulating tumor cells. During treatment follow-up, peripheral blood was periodically drawn from patients, and scRNA-seq or flow cytometry was used to dynamically track changes in the number of circulating tumor cells, providing guidance for efficacy evaluation and treatment regimen adjustments.

[0061] It should be noted that all steps involved in the embodiments of this application are performed by computer equipment. That is, after receiving the raw sequencing data of tumor tissue, the computer equipment identifies it through a preset algorithm and automatically constructs a personalized gene package. Subsequently, when receiving peripheral blood sequencing data after treatment of the target patient, the computer equipment can automatically identify the gene sequence of the B cell population and determine the presence status of the lesion based on the expression of the personalized gene package.

[0062] After the computer equipment completes the above process and obtains the detection results of the lesion's presence, the doctor can determine the patient's disease progression and provide a corresponding treatment plan based on the computer equipment's detection results, combined with the patient's medical history and other test results.

[0063] In summary, for target patients with diffuse large B-cell lymphoma, the first step is to obtain raw sequencing data of the tumor tissue. Then, based on this raw sequencing data, the malignant B-cell population within the tumor tissue is identified, and specifically expressed genes are screened to construct a personalized gene package for the target patient. For post-treatment patients, peripheral blood is collected and sequenced. Based on this peripheral blood sequencing data and B-cell characteristic genes, the gene sequences of the B-cell population within the peripheral blood sequencing data are first identified. After obtaining the B-cell gene sequences, the expression of the personalized gene package can be detected, thereby determining the status of minimal residual disease (MRD) in diffuse large B-cell lymphoma. This approach allows for the construction of personalized gene packages for different patients, and the expression results of these personalized gene packages in post-treatment peripheral blood sequencing data are used to determine the status of MRD in diffuse large B-cell lymphoma, thus improving the accuracy of detecting circulating minimal residual disease (MRD) in patients with diffuse large B-cell lymphoma.

[0064] This application also provides a device for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0065] This application provides a device for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma. Figure 11 This is a schematic diagram of a circulating minimal residual disease monitoring device for diffuse large B-cell lymphoma provided in an embodiment of this application. The device includes: Sequencing data acquisition module 1101 is used to acquire raw sequencing data of tumor tissue from the target patient; The gene package construction module 1102 is used to identify the malignant B cell population in the tumor tissue based on the original sequencing data, and to screen specifically expressed genes based on the malignant B cell population to construct a personalized gene package. The sequencing data acquisition module 1103 is used to acquire peripheral blood sequencing data of the target patient; the peripheral blood sequencing data is obtained by sequencing the peripheral blood of the patient after treatment; The cell population identification module 1104 is used to identify the gene sequence of the B cell population based on the peripheral blood sequencing data and B cell characteristic genes. Minimal residual disease identification module 1105 is used to detect the expression of personalized gene packages in the gene sequence of the B cell population to determine the status of minimal residual disease in the diffuse large B-cell lymphoma.

[0066] In summary, for target patients with diffuse large B-cell lymphoma, the first step is to obtain raw sequencing data of the tumor tissue. Then, based on this raw sequencing data, the malignant B-cell population within the tumor tissue is identified, and specifically expressed genes are screened to construct a personalized gene package for the target patient. For post-treatment patients, peripheral blood is collected and sequenced. Based on this peripheral blood sequencing data and B-cell characteristic genes, the gene sequences of the B-cell population within the peripheral blood sequencing data are first identified. After obtaining the B-cell gene sequences, the expression of the personalized gene package can be detected, thereby determining the status of minimal residual disease (MRD) in diffuse large B-cell lymphoma. This approach allows for the construction of personalized gene packages for different patients, and the expression results of these personalized gene packages in post-treatment peripheral blood sequencing data are used to determine the status of MRD in diffuse large B-cell lymphoma, thus improving the accuracy of detecting circulating minimal residual disease (MRD) in patients with diffuse large B-cell lymphoma.

[0067] Please see Figure 12 , Figure 12 This is a schematic diagram of the structure of an electronic device provided in an optional embodiment of the present invention. This electronic device can be a computer device used to execute the above-described method. Figure 12 As shown, the electronic device includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces).

[0068] The processor 10 may further include a hardware chip. This hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0069] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0070] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the use of the electronic device based on the display of a mini-program landing page. Furthermore, the memory 20 may include high-speed random access memory (RAM), and may also include non-transient memory, such as at least one disk storage device, flash memory device, or other non-transient solid-state storage device. The memory 20 may include volatile memory, such as RAM; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive; the memory 20 may also include combinations of the above types of memory.

[0071] The electronic device also includes a communication interface 30 for communicating with other devices or communication networks.

[0072] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma, characterized in that, The method includes: Obtain raw sequencing data of tumor tissue from the target patient; Based on the original sequencing data, the malignant B cell population in the tumor tissue was identified, and specific expression genes were screened based on the malignant B cell population to construct a personalized gene package. Peripheral blood sequencing data of the target patient was obtained; the peripheral blood sequencing data was obtained by sequencing the peripheral blood of the patient after treatment; Based on the peripheral blood sequencing data and B cell characteristic genes, the gene sequences of the B cell population were identified; The expression of personalized gene packs was detected in the gene sequence of the B cell population to determine the presence of minimal residual disease in the diffuse large B-cell lymphoma.

2. The method according to claim 1, characterized in that, The raw sequencing data of the tumor tissue includes at least one of spatial transcriptome sequencing data, single-cell RNA sequencing data, or single-nuclear RNA sequencing data.

3. The method according to claim 2, characterized in that, The process of screening for specifically expressed genes based on the malignant B cell population and constructing a personalized gene package includes: Differential gene expression and light chain expression analysis were performed on the raw sequencing data of the tumor tissue to identify malignant B cell populations and screen for genes specifically expressed in the malignant B cell populations to construct personalized gene packages; the specifically expressed genes are those that are highly expressed in the malignant B cell populations and low expressed in non-malignant B cells and other cell types.

4. The method according to claim 3, characterized in that, The specifically expressed genes include at least one of the following: DLBCL marker genes, oncogenes with abnormally high expression, and transcripts associated with B-cell receptor rearrangements.

5. The method according to any one of claims 1 to 4, characterized in that, The peripheral blood sequencing data were scRNA-seq and BCR-seq sequencing data. The step of identifying the gene sequence of the B cell population based on the peripheral blood sequencing data and B cell characteristic genes includes: Based on the B cell characteristic genes, malignant B cells or B cells with light chain restriction expression in BCR-seq are screened from the peripheral blood sequencing data, and gene expression sequences of suspected malignant B cells are obtained.

6. The method according to claim 5, characterized in that, Detecting the expression of personalized gene packages in the gene sequence of the B-cell population to determine the presence status of minimal residual disease in the diffuse large B-cell lymphoma includes: Based on the expression levels of differentially expressed genes in the personalized gene package in the gene sequence of the B cell population and the results of functional enrichment analysis of the B cell population, it is determined whether a circulating tumor cell population exists in the B cell population.

7. The method according to claim 5, characterized in that, The method further includes: B cell populations were sorted from the peripheral blood samples based on B cell-specific markers and light chain expression. Flow cytometry was used to detect the protein expression of at least two characteristic genes of the personalized gene package in the B cell population to determine the proportion of abnormal B cell population.

8. A device for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma, characterized in that, The device includes: Sequencing data acquisition module, used to acquire raw sequencing data of tumor tissue from the target patient; A gene package construction module is used to identify the malignant B cell population in the tumor tissue based on the original sequencing data, and to screen specifically expressed genes based on the malignant B cell population to construct a personalized gene package. The sequencing data acquisition module is used to acquire peripheral blood sequencing data of the target patient; the peripheral blood sequencing data is obtained by sequencing the peripheral blood of the patient after treatment; A cell population identification module is used to identify the gene sequence of a B cell population based on the peripheral blood sequencing data and B cell characteristic genes. The lesion status determination module is used to detect the expression of personalized gene packages in the gene sequence of the B cell population to determine the presence status of minimal residual lesions in the diffuse large B-cell lymphoma.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method for monitoring circulating minimal residual disease in diffuse large B-cell lymphoma as described in any one of claims 1 to 7.

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