Acute myelogenous leukemia first treatment course curative effect diagnosis model and construction method and application thereof

Abstract

The invention discloses an acute myelogenous leukemia first treatment course curative effect diagnosis model and a construction method and application thereof.The prognosis model is an acute myelogenous leukemia prognosis model based on eight clinical characteristics, and the construction method comprises the steps that clinical data from a patient in a dataset and RNA sequencing data of peripheral blood and bone marrow samples are combined with survival information to obtain a first treatment course curative effect diagnosis model of acute myelogenous leukemia; the method comprises the following steps: sequentially carrying out single-variable logistic regression analysis and multivariable logistic regression analysis on the basis of a single-variable logistic regression model, further weighting an expression level according to an obtained regression coefficient, and establishing a first treatment course curative effect diagnosis model of acute myelogenous leukemia based on eight clinical characteristics. The acute myelogenous leukemia first treatment course curative effect diagnosis model has a good evaluation effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of disease prognosis and molecular biology, and specifically relates to a diagnostic model for the efficacy of the first course of treatment of acute myeloid leukemia and a construction method and application 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 long-term environmental or occupational exposure to petrochemicals or benzene is recognized as 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] Currently, there is no large literature report on the prediction of the efficacy of the first course of treatment. For example, if the same patient has both good and poor prognostic biological indicators, how to predict the efficacy and survival period. Most of the existing AML prognostic indicators are still related to survival period, and there are no relevant reports on efficacy prediction, so this technical problem needs to be solved urgently. Summary of the invention

[0004] The present invention provides a diagnostic model for the efficacy of the first course of treatment for acute myeloid leukemia and a construction method and application thereof. The model provided by the present invention has good performance and stability and can provide guidance for the treatment selection and clinical decision-making of AML.

[0005] The present invention adopts the following technical solutions: The first aspect of the present invention is a diagnostic model for the efficacy of the first course of treatment for acute myeloid leukemia, wherein the diagnostic markers of the diagnostic model for the efficacy of the first course of treatment include age of onset, peripheral blood leukocyte count at onset, bone marrow primitive cell count at onset, CBFB::MYH11 fusion gene, TP53 mutation, CEBPA bZIP region mutation, myelodysplastic syndrome MDS-related gene mutation and the selection of induction chemotherapy regimen.

[0006] Furthermore, the myelodysplastic syndrome (MDS)-related gene mutation positivity is defined as: having two or more of the following genes: SRSF2, ZRSR2, SF3B1, ASXL1, BCOR, EZH2, U2AF1, STAG2, RUNX1 positivity.

[0007] Furthermore, the efficacy criteria of the first course of treatment efficacy diagnostic model for acute myeloid leukemia are as follows: the first course of treatment remission is composed of complete remission CR and complete remission CRi with incomplete hematological recovery; the CR is 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 .

[0008] Specifically, the selected diagnostic markers all refer to peripheral blood and bone marrow specimens at the time of onset of the disease.

[0009] Furthermore, the selection of the induction chemotherapy regimen is to define the bcl-2 inhibitor combined with intensive chemotherapy as a new regimen, and the others as old regimens.

[0010] The second aspect of the present invention provides an acute myeloid leukemia first course treatment efficacy diagnostic model for use in the initial diagnosis of acute myeloid leukemia.

[0011] The third aspect of the present invention is a method for constructing a diagnostic model for the efficacy of the first course of treatment for acute myeloid leukemia, which is used to predict the efficacy of myeloid leukemia patients, comprising 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) Data screening: The data collected in step (1) were combined with the results of efficacy evaluation, and variables related to the efficacy of the first course of treatment for acute myeloid leukemia patients were screened out through univariate logistic regression analysis. The probability value P of each variable was calculated using SPSS software; (3) Multivariate logistic regression: The results of step (2) with a P value less than 0.2 were included in the multivariate logistic regression analysis to obtain independent risk factors and their regression coefficients β. (4) Model creation: Use the R package to construct an AML first-course diagnosis model based on the independent risk factors obtained in step (3), and improve the nomogram and online calculator to obtain an acute myeloid leukemia first-course treatment efficacy diagnosis model.

[0012] Specifically, the R package is a toolkit provided by the R language, which includes a series of pre-written functions and data sets for data analysis, statistical modeling and visualization.

[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 genes related to the survival of acute myeloid leukemia patients include CBFB::MYH11 fusion gene, TP53 mutation, CEBPA bZIP region mutation, and MDS-related gene mutation.

[0015] The beneficial effects of the present invention are as follows: the variables required for the acute myeloid leukemia diagnostic model provided by the present invention are easy to obtain clinically, have a small economic burden, and are intuitively presented through a nomogram and an online calculator. Clinicians can select an induction regimen that is beneficial to the efficacy of the first course of treatment based on the model results. The model is simple and clear, easy to promote clinically, and therefore has good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a result analysis diagram of 561 patients in the training set of the present invention; Figure 2 This is a result analysis chart of 188 patients in the validation set of the present invention; Figure 3 It is a nomogram of the diagnostic model composed of 8 variables of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] 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.

[0019] The method for establishing the diagnosis model of the efficacy of the first course of treatment for acute myeloid leukemia of the present invention is described in detail in Example 1. Figure 1-Figure 3In the data, myelodysplastic syndrome is represented by MDS (Myelodysplastic syndromes); tumor protein p53 gene is represented by TP53 (Tumor Protein p53); myosin heavy chain (Myosin Heavy Chain) 11 core binding factor β is represented by CBF (MYH11: Core-binding factor beta); CEBPA bZIP domain mutation is represented by CEBPA bZIP (CCAAT / enhancer binding protein αbasic leucine zipper).

[0020] Example 1

[0021] (1) Data collection: Figure 1 As shown, the training set of the present invention includes clinical data of 561 AML patients and RNA sequencing data of peripheral blood or bone marrow samples.

[0022] (2) Screening variables: The data collected in step (1) were analyzed by univariate logistic regression to screen out variables related to the efficacy of the first course of treatment for patients with acute myeloid leukemia. The probability value (Probability Value) P of each variable was calculated using SPSS software; see Table 1.

[0023] (3) Multivariate logistic regression: The results of step (2) screening with P < 0.2 were included in the multivariate logistic regression analysis to obtain the independent risk factors and, as shown in Table 1; Figure 3 The eight independent risk factors shown are age at onset, peripheral blood leukocyte count at onset, hemoglobin count, CBFB::MYH11 fusion gene, TP53 mutation, CEBPA bZIP region mutation, and myelodysplastic syndrome (MDS)-related gene mutations.

[0024] Example 2 like Figure 1 and Figure 2 As shown, the areas under the curve of the training set and validation set of the diagnostic model obtained in Example 1 are 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The CI is Confidence interval, which means confidence interval, and the 95% CI is the 95% confidence interval. Figure 2As shown, the Hosmer-Lemeshow test results in the R package showed that the P values ​​were 0.993 and 0.335, respectively (the present invention utilizes the relevant functions and tools in the R package to obtain the P value results. The R package is a toolkit provided for the R language, which includes a series of pre-written functions and data sets, and can help users perform data analysis, statistical modeling, and visualization more efficiently), indicating that it has a good prompting effect on the efficacy of the first course of AML treatment.

[0025] like Figure 3 The nomogram of the diagnostic model is shown in Figure 3 The 8 independent risk factors shown in the figure are age at onset, peripheral blood leukocyte count at onset, bone marrow blast count at onset, CBFB::MYH11 fusion gene, TP53 mutation, CEBPA bZIP region mutation, and myelodysplastic syndrome (MDS)-related gene mutations. For each patient, the 8 variables are calculated according to the assignments shown in the figure, and the total score is finally calculated. The probability of remission in the first course of treatment is calculated based on the total score, as shown in Table 1 below.

[0026] Table 1

[0027] According to the results of multivariate Cox regression analysis in Table 1, a total of 8 variables were associated with AML survival, namely, age of onset, peripheral blood leukocyte count at onset, bone marrow primitive cell count at onset, CBFB::MYH11 fusion gene, TP53 mutation, CEBPA bZIP region mutation, and myelodysplastic syndrome (MDS)-related gene mutations.

[0028] 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 modification, use or adaptation of the present application, which follows the general principles of the present application and includes 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 claims.

[0029] It should be understood that the present invention is not limited to the exact construction that has 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 invention is limited only by the appended claims.

Claims

1. A diagnostic model for the efficacy of the first course of treatment for acute myeloid leukemia, characterized in that: The diagnostic markers of the first course of treatment efficacy diagnostic model include age of onset, peripheral blood leukocyte count at onset, bone marrow primitive cell count at onset, CBFB::MYH11 fusion gene, TP53 mutation, CEBPA bZIP region mutation, myelodysplastic syndrome MDS-related gene mutation and the choice of induction chemotherapy regimen.

2. The acute myeloid leukemia first course treatment efficacy diagnostic model according to claim 1, characterized in that: The myelodysplastic syndrome (MDS)-related gene mutation positivity is defined as: having two or more of the following genes: SRSF2, ZRSR2, SF3B1, ASXL1, BCOR, EZH2, U2AF1, STAG2, RUNX1 positivity.

3. The diagnostic model for the first course of treatment efficacy of acute myeloid leukemia according to claim 1, characterized in that: The efficacy criteria of the first course of treatment efficacy diagnostic model for acute myeloid leukemia are as follows: the first course of treatment remission is composed of complete remission CR and complete remission CRi with incomplete hematological recovery; the CR is 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 .

4. The diagnostic model for the efficacy of the first course of treatment for acute myeloid leukemia 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.

5. The acute myeloid leukemia first course treatment efficacy diagnostic model according to claim 1, characterized in that: The selection of the induction chemotherapy regimen is to define the bcl-2 inhibitor combined with intensive chemotherapy as a new regimen, and the others as old regimens.

6. An application of the first course of treatment efficacy diagnostic model for acute myeloid leukemia according to any one of claims 1 to 5, characterized in that: The application of the acute myeloid leukemia first course treatment efficacy diagnostic model in the initial diagnosis of acute myeloid leukemia.

7. A method for constructing a diagnostic model for the first course of treatment efficacy of acute myeloid leukemia according to any one of claims 1 to 5, characterized in that: Used to predict the efficacy 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) Data screening: The data collected in step (1) were combined with the results of efficacy evaluation, and variables related to the efficacy of the first course of treatment for acute myeloid leukemia patients were screened out through univariate logistic regression analysis. The probability value P of each variable was calculated using SPSS software; (3) Multivariate logistic regression: The results of step (2) with a P value less than 0.2 were included in the multivariate logistic regression analysis to obtain independent risk factors and their regression coefficients β. (4) Model creation: Use the R package to construct an AML first-course diagnosis model based on the independent risk factors obtained in step (3), and improve the nomogram and online calculator to obtain an acute myeloid leukemia first-course treatment efficacy diagnosis model.

8. The method for constructing a diagnostic model for the first course of treatment efficacy of acute myeloid leukemia according to claim 7, characterized in that: The R package is a toolkit provided by the R language, which includes a series of pre-written functions and data sets for data analysis, statistical modeling, and visualization.

9. The method for constructing a diagnostic model for the first course of treatment efficacy of acute myeloid leukemia 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.

10. The method for constructing a diagnostic model for the first course of treatment efficacy of acute myeloid leukemia according to claim 7, characterized in that: The genes related to the survival of acute myeloid leukemia patients include CBFB::MYH11 fusion gene, TP53 mutation, CEBPAbZIP region mutation, and MDS-related gene mutation.

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