Diagnostic marker of acute myelogenous leukemia prognosis model as well as application and prediction method thereof

Abstract

The invention discloses a diagnostic marker of an acute myelogenous leukemia prognosis model as well as application and a prediction method of the diagnostic marker, and relates to the field of acute myelogenous leukemia prognosis research. The prognosis model is a prognosis model of acute myelogenous leukemia based on eight clinical characteristics, namely the disease age, the LDH level during disease, the RUNX1 gene, the KMT2A gene, the TP53 gene, the DMNT3A gene, the CBFB:: MYH11 gene and whether the first course of treatment is relieved or not; the construction method comprises the following steps of: sequentially carrying out univariate Cox regression analysis, Kaplan-Meier survival analysis and multivariate Cox regression analysis on clinical data of a patient from a data set and RNA sequencing data of peripheral blood and bone marrow samples in combination with survival information, further weighting an expression level according to an obtained regression coefficient, and carrying out quantitative analysis on the expression level. And establishing a prognosis model of acute myelogenous leukemia based on eight clinical characteristics. The acute myelogenous leukemia prognosis model disclosed by the invention has a relatively good evaluation effect.

Description

Technical Field

[0001] The invention belongs to the technical field of disease prognosis and molecular biology, and specifically relates to a diagnostic marker of an acute myeloid leukemia prognosis model and an application and prediction method thereof. Background Art

[0002] Acute myeloid leukemia (AML) is a group of aggressive, heterogeneous malignant diseases and is the most common type of acute leukemia in adults. AML can affect people of all ages, but is most common in the elderly, with a median age of 68 years at diagnosis, and two-thirds of patients are over 55 years old. AML is mostly sporadic, but it is recognized that long-term environmental or occupational exposure to petrochemicals or benzene is a risk factor for the development of myeloid malignancies (such as AML or myelodysplastic syndrome), including patients with previous exposure to cytotoxic drugs or radiotherapy (ie, treatment-related AML). In addition, with the increasing understanding of hereditary hematological malignancies, it has been found that approximately 5% to 10% of AML is associated with germline predisposition.

[0003] Improvements in AML are related to many factors, including advances in optimal supportive care (ie, antifungal and antibacterial prophylaxis and treatment), decreases in transplant-related mortality (enabling more patients, especially older patients, to benefit from this curative approach), and increased access to effective and novel treatment options.

[0004] The current diagnosis and treatment standard is to give consolidation chemotherapy or hematopoietic stem cell transplantation according to the patient's risk stratification after induction therapy to achieve long-term survival. Although the remission rate and survival of adult AML have improved with the increase in new drug research and development in the past 10 years, the overall prognosis of AML patients is still poor, with a 5-year survival rate of less than 30%. The ELN2022 guidelines divide AML into three groups of low, medium and high risk based on fusion genes, gene mutations, chromosomes, etc., without including other characteristics of patients, such as age of onset, sensitivity to chemotherapy, related biochemical indicators, etc. At present, many scholars have also proposed that the ELN2022 guidelines can only objectively evaluate patients receiving intensive chemotherapy, but cannot fully stratify elderly and frail patients. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a diagnostic marker for an acute myeloid leukemia prognostic model and its application and prediction method. The prognostic model provided by the present invention has good risk stratification performance and stability, and can provide guidance for treatment selection and clinical decision-making of AML.

[0006] The present invention adopts the following technical scheme: a diagnostic marker of an acute myeloid leukemia prognosis model, wherein the diagnostic marker of the prognosis model includes age of onset, LDH level at onset, RUNX1 Gene, KMT2A Gene TP53 Gene, DMNT3A gene, CBFB::MYH11 Gene and whether the first course of treatment achieves remission; The acute myeloid leukemia prognosis model constructed using the above diagnostic markers is: Risk score = 0.033677 × age of onset + 0.000493 × LDH level at onset + 0.553400 × RUNX1 gene+ 0.820775 × KMT2A Gene+ 1.256855 × TP53 gene+ 0.311104 × DMNT3A Gene - 0.635064 × CBFB::MYH11 Gene - 0.930328× Remission was achieved in the first course of treatment.

[0007] Furthermore, in the acute myeloid leukemia prognosis model constructed by the diagnostic marker, if Risk score >1.986, it is judged as a high-risk group; if Risk score <1.169, it is judged as a low-risk group; if 1.169 <Risk <1.986, it is judged as a medium-risk group.

[0008] Furthermore, when the first course of treatment achieves remission, it consists of complete remission (CR) and complete remission (CRi) with incomplete hematological recovery; the CR means that when the bone marrow primitive cells are <5%, there are no primitive cells in the peripheral blood, and there are no leukemia invasion lesions outside the marrow; the neutrophil count is ≥1×10 9 / L, platelet count ≥100×10 9 / L; the CRi is when all CR criteria are met except that the neutrophil count is <1×10 9 / L or platelet count <100×10 9 / L .

[0009] Specifically, the leukemia is acute myeloid leukemia.

[0010] Specifically, the selected diagnostic markers all refer to peripheral blood and bone marrow specimens at the time of onset of the disease.

[0011] Application of diagnostic markers of acute myeloid leukemia prognostic model in the acute myeloid leukemia prognostic assessment system.

[0012] A method for predicting diagnostic markers of an acute myeloid leukemia prognostic model, used for predicting the risk of myeloid leukemia patients, comprises the following steps: (1) Data collection: clinical data of patients with acute myeloid leukemia and RNA sequencing data of peripheral blood or bone marrow samples; (2) Screening data: The data collected in step (1) are combined with the patient survival information to screen out genes related to the survival of acute myeloid leukemia patients through univariate Cox regression analysis; (3) Multivariate Cox regression: The P < 0.2 screened in step (2) was included in the multivariate Cox regression analysis to obtain independent risk factors and their regression coefficients β; (4) Weighted expression level: The independent risk factors obtained in step (3) were used to construct the AML prognostic model and improve the nomogram and online calculator; the expression levels were further weighted, specifically to establish the prognostic risk score formula: Risk score = 0.033677 × age at onset + 0.000493 × LDH level at onset + 0.553400 × RUNX1 gene+0.820775 × KMT2A Gene+ 1.256855 × TP53 gene+ 0.311104 × DMNT3A Gene-0.635064 × CBFB::MYH11 Gene - 0.930328 × Remission was achieved in the first course of treatment; if Risk score > 1.986, it was judged as a high-risk state; if Risk score < 1.169, it was judged as a low-risk state; if 1.169 < Risk <1.986, it was judged as a medium-risk state.

[0013] Specifically, the peripheral blood or bone marrow sample is the nucleated cells in the bone marrow of the patient when the disease occurs.

[0014] Specifically, the survival information includes survival time and survival status; the survival time refers to the time from the onset of disease to death or the last follow-up; the survival status refers to the state of death or survival.

[0015] Specifically, the genes related to survival of acute myeloid leukemia patients include RUNX1 Gene ,KMT2A Gene , TP53 Gene ,DMNT3A Gene and CBFB::MYH11 Gene.

[0016] The beneficial effects of the present invention are as follows: The variables required for the acute myeloid leukemia prognostic model provided by the present invention are clinically easy to obtain, with a small economic burden, and are presented intuitively and simply through a nomogram and an online calculator. The validation set proves that the risk stratification of the acute myeloid leukemia prognostic model of the present invention is superior to ELN2022, and is easy to promote clinically, so it has good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a result analysis chart of 603 patients in the training set of the present invention; Figure 2 This is a result analysis chart of 115 patients in the validation set of the present invention; Figure 3 The nomogram of the prognostic model composed of 8 variables of the present invention; Figure 4 This is a stratified diagram of 718 AML patients according to the prognostic model of the present invention; Figure 5 A comparison chart of the composition of the layered model of the present invention and ELN2022; Figure 6 This is a comparison chart of the survival between transplantation and non-transplantation in the prognostic stratification of the present invention; Figure 7 It is a survival curve diagram of the high-risk population transplanted and non-transplanted population of the present invention; Figure 8 The prognostic model diagram of the present invention is compared by performing subgroup analysis according to the induction scheme of the present invention; Fig. 9 This is a comparison chart of the prognostic model diagram of the present invention and the ELN2022 guideline stratification. DETAILED DESCRIPTION

[0018] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those generally understood by those of ordinary skill in the art to which the present invention belongs. It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the term "comprising" or "including" is used in this specification, it indicates the presence of features, steps, operations, devices, components, or combinations thereof.

[0019] The present invention is further described in conjunction with specific examples. The following examples are only for explaining the present invention and are not intended to limit the content thereof. If the specific experimental conditions are not specified in the examples, they are usually carried out under conventional conditions or under conditions recommended by the sales company; the materials and reagents used in the examples, unless otherwise specified, can be purchased through commercial channels.

[0020] The method for establishing the acute myeloid leukemia prognosis model of the present invention is described in detail through Example 1.

[0021] (1) Data collection The training set of the present invention includes clinical data of 603 AML patients and RNA sequencing data of peripheral blood or bone marrow samples.

[0022] (2) Screening variables: The data collected in step (1) were subjected to univariate Cox regression analysis to screen out variables related to the survival of patients with acute myeloid leukemia; see Table 1.

[0023] (3) Multivariate Cox regression: Filter step (2) P <0.2 was included in the multivariate Cox regression analysis, and the independent risk factors and their regression coefficients β were obtained, as shown in Table 1; Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed according to the characteristics, and the independent risk factors and their regression coefficients β were obtained, as shown in Table 1 and Figure 3 The 8 independent risk factors shown are age at onset, LDH level at onset, RUNX1 Gene, KMT2A Gene , TP53 Gene, DMNT3A gene, CBFB::MYH11 Genes and whether remission is achieved in the first course of treatment.

[0024] (4) Weighted expression level: Based on the independent risk factors obtained in step (3), the AML prognostic model was constructed and the nomogram and online calculator were improved; the expression levels were further weighted to establish the prognostic risk score formula: Risk score = 0.033677 × age at onset + 0.000493 × LDH level at onset + 0.553400 × RUNX1 Gene+0.820775 × KMT2A Gene+ 1.256855 × TP53 gene+ 0.311104 × DMNT3A Gene-0.635064 × CBFB::MYH11 Gene - 0.930328 × Remission was achieved in the first course of treatment. If Risk score > 1.986, it is judged as a high-risk state; if Risk score < 1.169, it is judged as a low-risk state; if 1.169 < Risk <1.986, it is judged as a medium-risk state.

[0025] Example 1 In Example 1, the obtained prognostic model predicted that the ROC of the training set patients at the 1st, 2nd and 3rd year points were 0.794, 0.757 and 0.764 respectively (as shown in Figure 1). Figure 1As shown, the Harrell's concordindex (C index) for the training set is 0.748 (95% confidence interval (CI) 0.713-0.783). Figure 1 Part (A) of Figure 1 Part (B) and Figure 1 The (C) part in the figure shows that the POC curves (Receiver Operating Characteristic Curve) of the training set at 1, 2, and 3 years are 0.794, 0.757, and 0.764, respectively, indicating good discrimination. The ROC of 115 patients in the validation set at 1, 2, and 3 years are 0.906, 0.839, and 0.870, respectively (e.g. Figure 2 As shown, Figure 2 Part (A) shows that the ROC of the validation set patients at the first year point was 0.906; Figure 2 Part (B) shows that the ROC of the validation set patients at the second year point is 0.839; Figure 2 Part (C) in the figure shows that the ROC of the validation set patients at the first year point is 0.870). Figure 1 and Figure 2 The results show that the prognostic model obtained by the present invention has a high sensitivity to the survival rate of acute myeloid leukemia.

[0026] like Figure 3 The nomogram of the prognostic model is shown in Figure 3 The 8 independent risk factors shown in are age at onset, KMT2A Gene, DMNT3A gene, Whether the first course of treatment achieves remission, CBFB::MYH11 Gene, LDH (lactate dehydrogenase) level at the time of disease onset, RUNX1 Gene and TP53 Gene, For each patient, the eight variables are assigned values ​​according to the figure, and the total score is finally calculated. The 1-year, 2-year, and 3-year survival probabilities are obtained based on the total score.

[0027] Kaplan-Meier survival analysis was performed on the training set and validation set in combination with OS. When Risk score was greater than 1.986, it was judged as a high-risk group; when Risk score was less than 1.169, it was judged as a low-risk group; when Risk was between the two, it was judged as a medium-risk group. The 718 patients were divided into three groups of low, medium and high risk according to the above grouping principle, and then the survival curves of different groups under the model of the present invention were compared. Figure 4 As shown (as Figure 4Part (A) is a survival curve diagram of three groups of people with low, medium and high risk according to the model of the present invention; Figure 4 Part (B) in the figure is an alluvial diagram for stratified comparison between the model of the present invention and ELN2022, i.e., a comparison diagram of the survival curves of the three groups of low, medium and high risk people in the two grouping methods). Figure 4 Part (A) shows the comparison of survival time among the three groups of people: low-risk, medium-risk, and high-risk. The overall survival time of patients in the low-risk group is significantly longer than that of patients in the medium-risk and high-risk groups, and the survival time of patients in the medium-risk group is longer than that of patients in the high-risk group. Figure 4 As shown in part (B), a survival comparison of 718 patients stratified according to ELN2022 was performed and it was found that the survival of patients in the low-risk group of the model of the present invention was longer than that of the low-risk group of ELN2022, while the survival of patients in the high-risk group of the model of the present invention was lower than that of patients in the high-risk group of ELN2022, indicating that the model of the present invention is better for the prognosis stratification of AML. Figure 5 A comparison chart of the composition of the layered model of the present invention and ELN2022; Figure 5 In the study, 718 patients were stratified by the model of the present invention and the ELN2022 guidelines for comparison. For example, some patients who originally belonged to the low-risk group of ELN2022 were divided into the high-risk group according to the Risk score in the model. From the distribution of the number of people, the three groups of the model of the present invention were more balanced.

[0028] Figure 6 As shown (as Figure 6 Part (A) shows the survival curves of low-risk transplanted and non-transplanted populations; Figure 6 Part (B) shows the survival curves of the intermediate-risk population and the non-transplant population. Figure 7 The survival curves of high-risk transplanted and non-transplanted populations are shown in Figure 1. Prognostic groups are grouped according to the Risk score model of the present invention, such as Figure 6 The (A) part is the low-risk group, which is divided into two groups according to whether or not the transplantation is performed. The results show that the subsequent hematopoietic stem cell transplantation has no benefit on survival in the low-risk group. Figure 6 Part (B) and Figure 7 They are divided into intermediate-risk and high-risk groups, respectively. The results show that intermediate- and high-risk patients can benefit from hematopoietic stem cell transplantation, suggesting that subsequent consolidation treatment plans can be guided according to prognostic stratification.

[0029] like Figure 8 and Fig. 9 As shown (as Figure 8 Part (A) is a comparison of the survival curves of the three groups of low-risk, medium-risk, and high-risk patients stratified by intensive chemotherapy. Figure 8 Part (B) shows the comparison of the survival curves of the three stratified groups of low-risk, medium-risk and high-risk groups using intensive chemotherapy combined with Bcl-2 inhibitors. Fig. 9This is a comparison chart of the survival curves of the three groups of low-risk, medium-risk and high-risk people in two stratified weak chemotherapy based on the Bcl-2 inhibitor of the present invention. According to the different induction chemotherapy regimens in the first course of treatment, they are divided into three groups, namely: Figure 8 In the intensive chemotherapy group in part (A), Figure 6 Part (B) of the study included the combination of intensive chemotherapy and Bcl-2 inhibitors. Fig. 9 The weak chemotherapy group based on Bcl-2 inhibitors. In the model of the present invention, according to the Risk score, there are differences in survival among the three groups of low, medium and high risk (indicated by the solid line), while the ELN2022 guideline stratification can only distinguish the strong chemotherapy group. There is no significant difference in survival among the three groups of low, medium and high risk in the two groups of strong chemotherapy combined with Bcl-2 inhibitors and weak chemotherapy based on Bcl-2 inhibitors ( Figure 6 ) (indicated by dotted line), indicating that in the era of new drugs, the model of the present invention is superior to the ELN2022 guidelines in both intensive chemotherapy and new drug combined chemotherapy induction therapy.

[0030] The results of univariate and multivariate Cox regression analysis of the training set are shown in Table 1: Table 1

[0031] According to the results of multivariate Cox regression analysis in Table 1, a total of 8 variables were associated with AML survival, namely, age at onset, LDH level at onset, RUNX1 gene, KMT2A gene, TP53 gene, DMNT3A gene, CBFB::MYH11 gene, and whether remission was achieved in the first course of treatment.

[0032] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0033] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A diagnostic marker for an acute myeloid leukemia prognosis model, characterized in that: The diagnostic markers of the prognostic model include age of onset, LDH level at onset, RUNX1 Gene, KMT2A Gene TP53 Gene, DMNT3A gene, CBFB::MYH11 Gene and whether the first course of treatment achieves remission; The acute myeloid leukemia prognosis model constructed using the above diagnostic markers is: Risk score = 0.033677 × age of onset + 0.000493 × LDH level at onset + 0.553400 × RUNX1 gene+ 0.820775 × KMT2A Gene+ 1.256855 × TP53 Gene+0.311104 × DMNT3A Gene - 0.635064 × CBFB::MYH11 Gene - 0.930328 × Remission was achieved in the first course of treatment.

2. The diagnostic marker for the prognostic model of acute myeloid leukemia according to claim 1, characterized in that: In the acute myeloid leukemia prognosis model constructed by the diagnostic marker, if Risk score > 1.986, it is judged as a high-risk group; if Risk score < 1.169, it is judged as a low-risk group; if 1.169 < Risk < 1.986, it is judged as a medium-risk group.

3. The diagnostic marker for the prognostic model of acute myeloid leukemia according to claim 1, characterized in that: When the first course of treatment achieves remission, it consists of complete remission (CR) and complete remission (CRi) with incomplete hematological recovery; CR means that when the bone marrow blasts are less than 5%, there are no blasts in the peripheral blood, and there are no leukemia invasion lesions outside the marrow; the neutrophil count is ≥1×10 9 / L, platelet count ≥100×10 9 / L; the CRi is when all CR criteria are met except that the neutrophil count is <1×10 9 / L or platelet count <100×10 9 / L .

4. The diagnostic marker of the acute myeloid leukemia prognosis model according to claim 1, characterized in that: The leukemia is acute myeloid leukemia.

5. The diagnostic marker of the acute myeloid leukemia prognosis model according to claim 1, characterized in that: The selected diagnostic markers all refer to peripheral blood and bone marrow specimens at the time of onset.

6. Use of the diagnostic marker of the acute myeloid leukemia prognosis model according to any one of claims 2 to 4 in an acute myeloid leukemia prognosis evaluation system.

7. A method for predicting diagnostic markers of the acute myeloid leukemia prognosis model according to any one of claims 1 to 3, characterized in that: Used to predict the risk of myeloid leukemia patients, including the following steps: (1) Data collection: clinical data of patients with acute myeloid leukemia and RNA sequencing data of peripheral blood or bone marrow samples; (2) Screening data: The data collected in step (1) are combined with the patient survival information to screen out genes related to the survival of acute myeloid leukemia patients through univariate Cox regression analysis; (3) Multivariate Cox regression: The P < 0.2 screened in step (2) was included in the multivariate Cox regression analysis to obtain independent risk factors and their regression coefficients β; (4) Weighted expression level: The independent risk factors obtained in step (3) were used to construct the AML prognostic model and improve the nomogram and online calculator; the expression levels were further weighted, specifically to establish the prognostic risk score formula: Risk score = 0.033677 × age at onset + 0.000493 × LDH level at onset + 0.553400 × RUNX1 Gene+0.820775 × KMT2A Gene+ 1.256855 × TP53 gene+ 0.311104 × DMNT3A Gene-0.635064 × CBFB::MYH11 Gene - 0.930328 × Remission was achieved in the first course of treatment; if Risk score > 1.986, it was judged as a high-risk state; if Risk score < 1.169, it was judged as a low-risk state; if 1.169 < Risk <1.986, it was judged as a medium-risk state.

8. The method for predicting diagnostic markers of an acute myeloid leukemia prognosis model according to claim 7, characterized in that: The peripheral blood or bone marrow sample refers to the nucleated cells in the bone marrow of the patient when the disease occurs.

9. The method for predicting diagnostic markers of a prognostic model for acute myeloid leukemia according to claim 7, characterized in that: The survival information includes survival time and survival status; the survival time refers to the time from the onset of disease to death or the last follow-up; the survival status refers to the state of death or survival.

10. The method for predicting diagnostic markers of an acute myeloid leukemia prognosis model according to claim 7, characterized in that: The genes associated with survival of acute myeloid leukemia patients include RUNX1 Gene ,KMT2A Gene ,TP53 Gene ,DMNT3A Gene and CBFB::MYH11 Gene.

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