Biomarkers related to prognosis of children acute lymphocytic leukemia and application of biomarkers

Abstract

The invention relates to the technical field of clinical molecular diagnosis and individualized tumor medicine, in particular to a group of biomarkers related to prognosis of children acute lymphocytic leukemia and application of the biomarkers. The invention aims to provide an ApopScore-based biomarker and a prognosis risk assessment model for quantifying the anti-apoptosis activity of a child B-ALL patient, predicting the recurrence risk, assessing the early curative effect and the long-term outcome and assisting the adjoint diagnosis. Therefore, the invention provides a new method for quantifying the apoptosis signal, and a prediction result is highly related to clinical outcomes (MRD, OS and CIR), so that the biomarker provided by the invention can be used as a companion diagnostic tool for guiding Venetoclax and drug combination; in addition, consistency verification is carried out on different people (East Asia, Southeast Asia, Europe and America, and Middle and American), and the clinical generalization performance is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of clinical molecular diagnosis and individualized oncology, in particular to a set of biomarkers related to the prognosis of childhood acute lymphoblastic leukemia and the application thereof. BACKGROUND

[0002] Childhood acute lymphoblastic leukemia (childhood ALL) is the most common childhood malignancy worldwide, among which B-cell type acute lymphoblastic leukemia is the most common. With the development of multi-drug combination chemotherapy regimens, the cure rate can reach more than 80%, but about 15%-20% of patients will relapse, and the prognosis after relapse is very poor.

[0003] Traditional biomarkers are usually based on a single gene or a small set of genes (qPCR or immunodetection) for risk assessment. There are currently reports using GSVA, gene sets or transcriptional regulatory networks (such as NetBID2) to infer sample pathway activity and for biomarker discovery or stratification studies (such as using the activity score of the LCK gene to infer sensitivity to the small molecule inhibitor dasatinib in childhood T-cell acute lymphoblastic leukemia (childhood T-ALL)). The current prognosis model for childhood acute lymphoblastic leukemia has the following defects:

[0004] Insufficient specificity and heterogeneity of origin: most of the prognosis models are based on population transcriptome or specific analysis of a small number of gene sets, and do not systematically integrate specific gene sets (such as "relapse-related apoptosis pathway") obtained from single-cell and population high-throughput sequencing, resulting in limited applicability of the model in predicting chemotherapy resistance.

[0005] Poor operability and clinical translation: lack of clear and repeatable detection procedures (from sample type, RNA processing, gene set definition, NetBID2 activity matrix to GSVA score and threshold setting), which is not conducive to standardized application and regulatory compliance between different centers.

[0006] Insufficient evidence for drug guidance: although some studies suggest that the apoptosis signaling pathway is related to drug resistance, they do not provide a scoring system based on single-cell evidence and verified by independent large cohorts to directly guide whether to choose a combination strategy that includes targeted anti-apoptosis pathway combined with chemotherapy. In addition, existing studies are mostly limited to single-center or single-population, lacking cross-racial and multi-cohort verification, making it difficult to be widely promoted to clinical practice.

[0007] Limited dynamic monitoring capability: existing detection is usually one-time sequencing or marker detection, lacking longitudinal sample (diagnosis-relapse-treatment) verification of quantitative indicators reflecting clonal evolution and metabolic activity changes.

[0008] Apoptotic signaling is an important determinant of the sensitivity of leukemia cells to chemotherapy and targeted therapy. Existing risk stratification systems fail to quantify the degree of apoptosis dependence of patients, making it difficult to predict the differences in the responses of different patients to leukemia treatment, especially BCL-2 inhibitors. Studies have shown that relapsed B-ALL samples generally exhibit down-regulation of the apoptosis pathway, including endogenous apoptosis, exogenous apoptosis, and DNA damage response-related apoptosis. Therefore, it is necessary to develop a scoring method that can systematically quantify the apoptosis capacity and predict the clinical prognosis. SUMMARY

[0009] The purpose of the present application is to provide a set of biomarkers related to the prognosis of childhood acute lymphoblastic leukemia and their applications, in order to solve the problems existing in the prior art. The present application aims to provide an ApopScore biomarker-based and prognostic risk assessment model to quantify the anti-apoptotic activity of childhood ALL patients for predicting the risk of relapse, assessing early efficacy and long-term outcome, and assisting in companion diagnostics (guiding the application of targeted anti-apoptotic protein antagonists, such as BCL-2 inhibitors (e.g. Venetoclax)).

[0010] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0011] The present application provides a biomarker related to the prognosis of childhood acute lymphoblastic leukemia, which comprises the following genes:

[0012] ADA, ADAM8, ANXA6, ATAD5, ATF4, ATM, ATP2A3, AURKB, BBC3, BCL2, BCL3, BRCA1, BRCA2, BTK, CASP8AP2, CCR7, CD24, CD38, CD70, CD74, CDKN1A, CDKN2A, CIB1, CRIP1, CUL3, DAPK1, DAXX, DDIT4, DDX3X, DDX5, DNAJC10, DOCK8, E2F1, E2F2, FHIT, FNIP1, FOXO3, FOXP1, GABARAP, GGCT, GSK3B, HCLS1, HIF1A, HINT1, HMGB2, IER3, IFI16, IFI6, IL1B, IL7R, IRF7, IRS2, ITPR1, JAK3, LGALS9, LYN, MAP2K4, MAP3K5, MCL1, MEF2C, MIF, MKNK2, MSH6, NBN, NF1, NFKBIZ, ORMDL3, PIK3CD, PIK3R1, PMAIP1, PML, PPP1R15A, PRKD2, PRKDC, PSEN1, PTPRC, PYCARD, RAG1, RAPGEF2, RB1CC1, SELENOK, SHISA5, SIVA1, SLC39A10, SMAD3, SMAD4, SOD2, SPI1, SPN, ST6GAL1, STK4, TGFB1, TMEM109, TNF, TNFRSF1B, TP53, TSC22D3, USP28, and WWOX.

[0013] Preferably, the leukemia comprises acute lymphoblastic leukemia.

[0014] The present application provides use of a preparation for detecting the above-mentioned childhood leukemia prognosis biomarker in preparation of a product for predicting prognosis of childhood leukemia.

[0015] Preferably, the leukemia comprises acute lymphoblastic leukemia.

[0016] The present application provides use of the above-mentioned childhood leukemia prognosis biomarker in construction of a childhood leukemia prognosis risk score model.

[0017] Preferably, the leukemia comprises acute lymphoblastic leukemia.

[0018] The application provides a children's leukemia prognosis risk assessment model, which uses the expression level of the children's leukemia prognosis biomarker as an input variable, obtains an activity matrix of the children's leukemia prognosis biomarker by using NetBID2, and then calculates an enrichment score by using GSVA, and predicts the children's leukemia prognosis according to the obtained enrichment score.

[0019] The median of the enrichment score is used as a critical value, when the risk score is greater than or equal to the median, it is judged as a low-risk group, and when the risk score is less than the median, it is judged as a high-risk group.

[0020] The application provides an application of the children's leukemia prognosis risk assessment model in constructing a children's leukemia prognosis system or device, and the children's leukemia patients are grouped according to the results of the children's leukemia prognosis risk assessment model, and the prognosis of the children's leukemia patients is predicted.

[0021] The application provides an application of the children's leukemia prognosis biomarker or the children's leukemia prognosis risk assessment model in preparing an efficacy evaluation product.

[0022] The application provides an application of the children's leukemia prognosis biomarker or the children's leukemia prognosis risk assessment model in preparing a drug guiding product for children's leukemia.

[0023] The application discloses the following technical effects:

[0024] The application is based on the relapse down-regulated mitochondrial gene set identified by single cell (Dx / Rel), and the ApopScore calculated by GSVA after deducing the activity by using NetBID2 is used as an objective and repeatable sample-level tumor cell anti-apoptosis activity index; and an integrated technical solution is provided from sample processing, gene panel or sequencing / chip detection, calculation process, threshold grouping to clinical decision (prognosis stratification, venetoclax applicability and combination drug recommendation), so as to make up for the deficiencies of the prior art in specificity, operability and clinical availability. The application aims to provide an ApopScore biomarker and a prognosis risk assessment model, which quantize the anti-apoptosis activity of children's B-ALL patients, are used for predicting the relapse risk, evaluating the early efficacy and long-term outcome, and assisting in companion diagnosis (guiding the application of the target anti-apoptosis protein antagonist BCL-2 inhibitor (such as Venetoclax)). It can be seen that the application provides a new method for quantizing the apoptosis signal, and the prediction result is highly related to the clinical outcome (MRD, OS and CIR), so that the biomarker provided by the application can be used as a companion diagnostic tool to guide the application of Venetoclax and combination drugs; and the specific embodiments of the application have been verified in different populations (East Asia and Europe and the United States), and have strong clinical popularization.

[0025] The application also creatively provides a childhood leukemia prognosis risk assessment model, which quantifies the apoptosis potential of childhood B-ALL patients by integrating single-cell transcriptome differential expression and GO apoptosis pathway annotation, obtaining the activity matrix of the biomarker by using NetBID2, and calculating the enrichment score by using GSVA, and reveals the mechanism correlation of low ApopScore→apoptosis inhibition→chemotherapy and target drug resistance. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0027] Figure 1 The childhood leukemia prognosis risk assessment model and the prediction effect; wherein A is the overall process of the childhood leukemia prognosis risk assessment model, B is the CIR (cumulative incidence) and OS curve (overall survival curve) of the CCCG-ALL-2015 cohort, and C is the EFS (event-free survival) of the TARGET cohort. DETAILED DESCRIPTION

[0028] The various exemplary embodiments of the present application will now be described in detail, which should not be considered as limiting the present application, but should be understood as a more detailed description of certain aspects, characteristics and embodiments of the present application.

[0029] It should be understood that the terms described in the present application are only for describing the specific embodiments, and are not used to limit the present application. In addition, for the numerical range in the present application, it should be understood that each intermediate value between the upper limit and the lower limit of the range is also specifically disclosed. Each smaller range between any stated value or intermediate value within the stated range, and any other stated value or intermediate value within the stated range, is also included in the present application. The upper limit and the lower limit of these smaller ranges can be independently included or excluded from the range.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application pertains. Although preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials in connection with which the documents are concerned. In the event of any conflict between the content of this specification and any document incorporated by reference, the content of this specification will control.

[0031] Many modifications and variations of the specific embodiments of the application can be practiced in accordance with the teachings of the description of the application. Other implementations of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The specification and examples given are exemplary only.

[0032] As used herein, the terms "comprises", "comprising", "includes", "including", "has", "having", "contains", "containing", or variations thereof, are intended to be open-ended terms that mean inclusion, but not limited to, the listed material or step.

[0033] The overall design process of ApopScore risk assessment system (ApopScore scoring model) is shown as A in Figure 1 , which shows the overall process of constructing the ApopScore scoring system based on single-cell transcriptome (scRNA-seq) and bulk transcriptome (bulk RNA-seq) data, including:

[0034] (1) Diagnosis and relapse stage paired single-cell RNA sequencing data acquisition;

[0035] (2) Apoptosis-related biological process (GO) enrichment analysis and differential gene set screening;

[0036] (3) Construction of apoptosis signaling pathway network (including endogenous and exogenous apoptosis pathways);

[0037] (4) Transcription factor and signal molecule activity inference based on NetBID2;

[0038] (5) Calculate ApopScore by GSVA and perform patient stratification analysis;

[0039] (6) Multi-cohort prognosis verification and efficacy correlation analysis.

[0040] Example 1: Obtaining biomarkers

[0041] 1. Sample information

[0042] Bone marrow mononuclear cells from newly diagnosed and relapsed stage patients with childhood acute lymphoblastic leukemia (ALL) from the Chinese Children's Cancer Group-Acute Lymphoblastic Leukemia Study Group-2015 Program (CCCG-ALL-2015-1 cohort) were selected. The sample information is shown in Table 1.

[0043] Table 1: Information of patients with childhood acute lymphoblastic leukemia (ALL, CCCG-ALL-2015-1 cohort)

[0044]

[0045] Note: Common B is acute lymphoblastic leukemia (B lymphocyte series), and PreB is pre-b acute lymphoblastic leukemia.

[0046] 2. Experimental platform

[0047] Single-cell transcriptome (single-cell RNA-seq) sequencing platform: 10x Genomics Chromium system;

[0048] Data analysis environment: R 4.3.1 and Python 3.10, analysis packages including Seurat v4.3.0, NetBID2, GSVA, clusterProfiler (metascape), etc.

[0049] 3. Sequencing of bone marrow mononuclear cells of samples in Table 1 using 10x Genomics Chromium system.

[0050] 4. Differential expression analysis

[0051] Differential gene analysis was performed on the scRNA-seq data of the relapse samples. The FindMarkers function of the Seurat software was used to compare the gene expression levels in the newly diagnosed (Dx) and relapsed (Rel) leukemia cell populations, and to identify differentially expressed genes (threshold: log2FC>0.25, adj.P<0.05).

[0052] Under the R language environment, the specific calculation process is as follows:

[0053] DEGs<-FindMarkers(ball, ident.1="DX", ident.2="Rel", test.use="wilcox", min.pct=0.1, logfc.threshold=0.25, only.pos=FALSE, verbose=TRUE);

[0054] Wherein, ball is the single-cell transcriptome Seurat object of leukemia cells of a certain sample, and DX and Rel are cells derived from newly diagnosed or relapse.

[0055] Functional enrichment analysis was performed using clusterProfiler (metascape).

[0056] The results show that: down-regulated genes are significantly enriched in apoptosis-related signaling pathways, including GO:0097190 (apoptotic signaling pathway), GO:2000106 (regulation of leukocyte apoptotic process), GO:0070228 (regulation of lymphocyte apoptotic process), GO:0008630 (intrinsic apoptotic signaling pathway in response to DNA damage), GO:0097191 (extrinsic apoptotic signaling pathway), GO:2000107 (negative regulation of leukocyte apoptotic process). Extract the genes related to apoptosis, and constitute the apoptosis activity gene set (ApopSig99 gene set), which contains 99 genes, and the specific genes are as follows:

[0057] ADA, ADAM8, ANXA6, ATAD5, ATF4, ATM, ATP2A3, AURKB, BBC3, BCL2, BCL3, BRCA1, BRCA2, BTK, CASP8AP2, CCR7, CD24, CD38, CD70, CD74, CDKN1A, CDKN2A, CIB1, CRIP1, CUL3, DAPK1, DAXX, DDIT4, DDX3X, DDX5, DNAJC10, DOCK8, E2F1, E2F2, FHIT, FNIP1, FOXO3, FOXP1, GABARAP, GGCT, GSK3B, HCLS1, HIF1A, HINT1, HMGB2, IER3, IFI16, IFI6, IL1B, IL7R, IRF7, IRS2, ITPR1, JAK3, LGALS9, LYN, MAP2K4, MAP3K5, MCL1, MEF2C, MIF, MKNK2, MSH6, NBN, NF1, NFKBIZ, ORMDL3, PIK3CD, PIK3R1, PMAIP1, PML, PPP1R15A, PRKD2, PRKDC, PSEN1, PTPRC, PYCARD, RAG1, RAPGEF2, RB1CC1, SELENOK, SHISA5, SIVA1, SLC39A10, SMAD3, SMAD4, SOD2, SPI1, SPN, ST6GAL1, STK4, TGFB1, TMEM109, TNF, TNFRSF1B, TP53, TSC22D3, USP28, and WWOX.

[0058] Example 2 Validation of biomarkers

[0059] 1. Sample information

[0060] The samples of this example include the CCCG-ALL-2015-2 cohort (China Children’s Cancer Group, n=426) and the TARGET cohort (US / Europe, n=207, https: / / gdc.cancer.gov / content / target-all-publications-summary), a total of 733 samples.

[0061] The information of the CCCG-ALL-2015-2 cohort is shown in Table 2; the information of the TARGET cohort is shown in Table 3.

[0062] Table 2 Information of the CCCG-ALL-2015-2 cohort

[0063]

[0064] Table 3 Information of TARGET cohort

[0065]

[0066] 2. Experimental platform

[0067] Bulk RNA-seq sequencing platform: Illumina NovaSeq 6000;

[0068] Data analysis environment: R 4.3.1 and Python 3.10, analysis packages include Seurat v4.3.0, NetBID2, GSVA, clusterProfiler (metascape), etc.

[0069] 3. Experimental methods

[0070] 3.1. Detection

[0071] Illumina NovaSeq 6000 was used to perform transcriptome sequencing on bone marrow mononuclear cells of samples in Table 2, and transcriptome sequencing results of the TARGET cohort (Table 3).

[0072] 3.2. NetBID2 and GSVA scoring

[0073] To eliminate sample batch effects and nonlinear signal pathway differences, the NetBID2 software was used to infer the activity matrix of transcription factors and key genes from bulk RNA-seq, and generate a gene activity score matrix (TF / SIG activity matrix) for each patient. The ApopSig99 gene set was input into the GSVA algorithm, and the ApopScore score at the sample level was calculated based on the activity matrix generated by NetBID2. The GSVA algorithm refers to the literature “GSVA: gene set variation analysis for microarray and RNA-Seq data”.

[0074] The specific calculation process under the R language environment is as follows:

[0075] ApopScore <- gsva(matrix, ApopSig99, method = "ssgsea", ssgsea.norm = TRUE, verbose = TRUE);

[0076] Wherein, ApopSig99 is a gene set, and matrix is a gene activity matrix obtained by transcriptome sequencing.

[0077] ApopScore score range: -0.40~+0.39; high score represents the activation of apoptosis signal.

[0078] 3.3, grouping and statistical analysis

[0079] The samples were divided into high ApopScore group and low ApopScore group according to the median of ApopScore, wherein the median of ApopScore is 0.02.

[0080] Then, Kaplan-Meier survival curves (OS, CIR) were drawn according to the ApopScore scores of the samples, and cross-cohort validation was performed.

[0081] 4, results and analysis

[0082] 4.1, correlation between ApopScore score and prognosis

[0083] The correlation between ApopScore score and prognosis is shown in B-C of Figure 1 The results show that in the CCCG-ALL-2015-2 cohort, the cumulative incidence of relapse (CIR) of patients in the low ApopScore group is significantly higher than that in the high ApopScore group (P=0.005), and the overall survival rate (OS) of patients in the high ApopScore group is significantly better than that in the low ApopScore group (P=0.003), which indicates that leukemia cells with high apoptosis signal activity are more easily cleared by chemotherapy, thereby having better long-term survival prognosis; the results show that the prediction consistency of the ApopScore99 gene set provided by the present application is high, and the prediction effect is at an excellent level.

[0084] 4.2, cross-population validation

[0085] In the TARGET cohort (European and American cohort), high ApopScore patients showed better EFS (P<0.001).

[0086] In conclusion, the ApopScore99 gene set provided by the present application can effectively predict the prognosis of childhood leukemia, and has consistency and stability across races and regions.

[0087] The above-described embodiments are only preferred modes of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope of the present application as defined by the claims.

Claims

1. A biomarker associated with the prognosis of childhood acute lymphoblastic leukemia, characterized in that, The biomarkers include the following genes: ADA, ADAM8, ANXA6, ATAD5, ATF4, ATM, ATP2A3, AURKB, BBC3, BCL2, BCL3, BRCA1, BRCA2, BTK, CASP8AP2, CCR7, CD24, CD38, CD70, CD74, CDKN1A, CDKN2A, CIB1, CRIP1, CUL3, DAPK1, DAXX, DDIT4, DDX3X, DDX5, DNAJC10, DOCK8, E2F1, E2F2, FHIT, FNIP1, FOXO3, FOXP1, GABARAP, GGCT, GSK3B, HCLS1, HIF1A, HINT1, HMGB2, IER3, IFI16, IFI6, IL1B, IL7R, IRF7, IRS 2. ITPR1, JAK3, LGALS9, LYN, MAP2K4, MAP3K5, MCL1, MEF2C, MIF, MKNK2, MSH6, NBN, NF1, NFKBIZ, ORMDL3, PIK3CD, PIK3R1, PMAIP1, PML, PPP1R15A, PRKD2, PRKDC, PSEN1, PTPRC, P YCARD, RAG1, RAPGEF2, RB1CC1, SELENOK, SHISA5, SIVA1, SLC39A10, SMAD3, SMAD4, SOD2, SPI1, SPN, ST6GAL1, STK4, TGFB1, TMEM109, TNF, TNFRSF1B, TP53, TSC22D3, USP28 and WWOX.

2. The prognostic biomarker for childhood leukemia according to claim 1, characterized in that, The leukemia mentioned includes acute lymphoblastic leukemia.

3. The use of the formulation of the childhood leukemia prognostic biomarker of claim 1 in the preparation of products for predicting the prognosis of childhood leukemia.

4. The application according to claim 3, characterized in that, The leukemia mentioned includes acute lymphoblastic leukemia.

5. The application of the prognostic biomarker for childhood leukemia as described in claim 1 in constructing a prognostic risk scoring model for childhood leukemia.

6. The application according to claim 5, characterized in that, The leukemia mentioned includes acute lymphoblastic leukemia.

7. A prognostic risk assessment model for childhood leukemia, characterized in that, The model uses the expression level of the prognostic biomarker for childhood leukemia as described in claim 1 as the input variable, obtains the activity matrix of the prognostic biomarker for childhood leukemia using NetBID2, then calculates the enrichment score using GSVA, and predicts the prognosis of childhood leukemia based on the obtained enrichment score. The median enrichment score was used as the cutoff value. When the risk score was greater than or equal to the median, the group was classified as low-risk, and when the risk score was less than the median, the group was classified as high-risk. The prognosis of the high-risk group was poor.

8. The application of the childhood leukemia prognostic risk assessment model according to claim 7 in constructing a childhood leukemia prognostic system or device, characterized in that, Based on the results of the childhood leukemia prognostic risk assessment model, childhood leukemia patients are grouped to predict their prognosis.

9. The application of the prognostic biomarker for childhood leukemia as described in claim 1 or the prognostic risk assessment model for childhood leukemia as described in claim 7 in the preparation of efficacy evaluation products.

10. The application of the prognostic biomarker for childhood leukemia as described in claim 1 or the prognostic risk assessment model for childhood leukemia as described in claim 7 in the preparation of products that guide the use of medications for childhood leukemia.

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